statistics phd thesis topics

Peter (Haoxuan) Wu - “Advances in adaptive and deep Bayesian state-space models”

Dissertation Advisor: David Matteson

Initial job placement: Quantitative Researcher - DRW

Grace Deng - “Generative models and Bayesian spillover graphs for dynamic networks”

Initial job placement: Data Scientist - Research at Google

Samriddha Lahiry - “Some problems of asymptotic quantum statistical inference”

Dissertation Advisor: Michael Nussbaum

Initial job placement: Postdoctoral Fellow - Harvard University

Yaosheng Xu - “WWTA load-balancing for parallel-server systems with heterogeneous servers and multi-scale heavy traffic limits for generalized Jackson networks”

Dissertation Advisor: Jim Dai

Initial job placement: Applied Scientist - Amazon

Seth Strimas-Mackey - “Latent structure in linear prediction and corpora comparison”

Dissertation Advisor: Marten Wegkamp and Florentina Bunea

Initial job placement: Data Scientist at Google

Tao Zhang - “Topics in modern regression modeling”

Dissertation Advisor: David Ruppert and Kengo Kato

Initial job placement: Quantitative Researcher - Point72

Department of Statistics – Academic Commons Link to Recent Ph.D. Dissertations (2011 – present)

2022 Ph.D. Dissertations

Andrew Davison

Statistical Perspectives on Modern Network Embedding Methods

Sponsor: Tian Zheng

Nabarun Deb

Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing

Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee

Elliot Gordon Rodriguez

Advances in Machine Learning for Compositional Data

Sponsor: John Cunningham

Charles Christopher Margossian

Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling

Sponsor: Andrew Gelman

Alejandra Quintos Lima

Dissertation TBA

Sponsor: Philip Protter

Bridgette Lynn Ratcliffe

Statistical approach to tagging stellar birth groups in the Milky Way

Sponsor: Bodhisattva Sen

Chengliang Tang

Latent Variable Models for Events on Social Networks

On Recovering the Best Rank-? Approximation from Few Entries

Sponsor: Ming Yuan

Sponsor: Sumit Mukherjee

2021 Ph.D. Dissertations

On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods

Sponsor: Liam Paninski

Advances in Statistical Machine Learning Methods for Neural Data Science

Milad Bakhshizadeh

Phase retrieval in the high-dimensional regime

Chi Wing Chu

Semiparametric Inference of Censored Data with Time-dependent Covariates

Miguel Angel Garrido Garcia

Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles

Sponsor: Ioannis Karatzas

Rishabh Dudeja

High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices

Sponsor: Arian Maleki

Statistical Learning for Process Data

Sponsor: Jingchen Liu

Toward a scalable Bayesian workflow

2020 Ph.D. Dissertations

Jonathan Auerbach

Some Statistical Models for Prediction

Sponsor: Shaw-Hwa Lo

Adji Bousso Dieng

Deep Probabilistic Graphical Modeling

Sponsor: David Blei

Guanhua Fang

Latent Variable Models in Measurement: Theory and Application

Sponsor: Zhiliang Ying

Promit Ghosal

Time Evolution of the Kardar-Parisi-Zhang Equation

Sponsor: Ivan Corwin

Partition-based Model Representation Learning

Sihan Huang

Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates

Peter JinHyung Lee

Spike Sorting for Large-scale Multi-electrode Array Recordings in Primate Retina

Statistical Analysis of Complex Data in Survival and Event History Analysis

Multiple Causal Inference with Bayesian Factor Models

New perspectives in cross-validation

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Browsing FAS Theses and Dissertations by FAS Department "Statistics"

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A Grand Journey of Statistical Hierarchical Modelling 

Advances in empirical bayes modeling and bayesian computation , advances in statistical network modeling and nonlinear time series modeling , advances in the normal-normal hierarchical model , analysis, modeling, and optimal experimental design under uncertainty: from carbon nano-structures to 3d printing , bayesian biclustering on discrete data: variable selection methods , bayesian learning of relationships , a bayesian perspective on factorial experiments using potential outcomes , building interpretable models: from bayesian networks to neural networks , causal inference under network interference: a framework for experiments on social networks , complications in causal inference: incorporating information observed after treatment is assigned , diagnostic tools in missing data and causal inference on time series , dilemmas in design: from neyman and fisher to 3d printing , distributed and multiphase inference in theory and practice: principles, modeling, and computation for high-throughput science , essays in causal inference and public policy , expediting scientific discoveries with bayesian statistical methods , exploring objective causal inference in case-noncase studies under the rubin causal model , exploring the role of randomization in causal inference , extensions of randomization-based methods for causal inference , g-squared statistic for detecting dependence, additive modeling, and calibration concordance for astrophysical data .

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Statistics PhD theses

2015 onwards.

PhD Program information

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests. Many students are involved in interdisciplinary research. Students may also have the option to pursue a designated emphasis (DE) which is an interdisciplinary specialization:  Designated Emphasis in Computational and Genomic Biology ,  Designated Emphasis in Computational Precision Health ,  Designated Emphasis in Computational and Data Science and Engineering . The program requires four semesters of residence.

Normal progress entails:

Year 1 . Perform satisfactorily in preliminary coursework. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity. Years 2-3 . Continue coursework. Find a thesis advisor and an area for the oral qualifying exam. Formally choose a chair for qualifying exam committee, who will also serve as faculty mentor separate from the thesis advisor.  Pass the oral qualifying exam and advance to candidacy by the end of Year 3. Present research at BSTARS each year. Years 4-5 . Finish the thesis and give a lecture based on it in a department seminar.

Program Requirements

• Qualifying Exam

Course work and evaluation

Preliminary stage: the first year.

Effective Fall 2019, students are expected to take four semester-long courses for a letter grade during their first year which should be selected from the core first-year PhD courses offered in the department: Probability (204/205A, 205B,), Theoretical Statistics (210A, 210B), and Applied Statistics (215A, 215B). These requirements can be altered by a member of the PhD Program Committee (in consultation with the faculty mentor and by submitting a graduate student petition ) in the following cases:

• Students primarily focused on probability will be allowed to substitute one semester of the four required semester-long courses with an appropriate course from outside the department.
• Students may request to postpone one semester of the core PhD courses and complete it in the second year, in which case they must take a relevant graduate course in their first year in its place. In all cases, students must complete the first year requirements in their second year as well as maintain the overall expectations of second year coursework, described below. Some examples in which such a request might be approved are described in the course guidance below.
• Students arriving with advanced standing, having completed equivalent coursework at another institution prior to joining the program, may be allowed to take other relevant graduate courses at UC Berkeley to satisfy some or all of the first year requirements

Requirements on course work beyond the first year

Students entering the program before 2022 are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year , students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge. 

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U).  Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

First year course work: For the purposes of satisfactory progression in the first year, grades in the core PhD courses are evaluated as: A+: Excellent performance in PhD program A: Good performance in PhD program A-: Satisfactory performance B+: Performance marginal, needs improvement B: Unsatisfactory performance

First year and beyond: At the end of each year, students must meet with his or her faculty mentor to review their progress and assess whether the student is meeting expected milestones. The result of this meeting should be the completion of the student’s annual review form, signed by the mentor ( available here ). If the student has a thesis advisor, the thesis advisor must also sign the annual review form.

Guidance on choosing course work

Choice of courses in the first year: Students enrolling in the fall of 2019 or later are required to take four semesters of the core PhD courses, at least three of which must be taken in their first year. Students have two options for how to schedule their four core courses:

• Option 1 -- Complete Four Core Courses in 1st year: In this option, students would take four core courses in the first year, usually finishing the complete sequence of two of the three sequences.  Students following this option who are primarily interested in statistics would normally take the 210A,B sequence (Theoretical Statistics) and then one of the 205A,B sequence (Probability) or the 215A,B sequence (Applied Statistics), based on their interests, though students are allowed to mix and match, where feasible. Students who opt for taking the full 210AB sequence in the first year should be aware that 210B requires some graduate-level probability concepts that are normally introduced in 205A (or 204).
• Option 2 -- Postponement of one semester of a core course to the second year: In this option, students would take three of the core courses in the first year plus another graduate course, and take the remaining core course in their second year. An example would be a student who wanted to take courses in each of the three sequences. Such a student could take the full year of one sequence and the first semester of another sequence in the first year, and the first semester of the last sequence in the second year (e.g. 210A, 215AB in the first year, and then 204 or 205A in the second year). This would also be a good option for students who would prefer to take 210A and 215A in their first semester but are concerned about their preparation for 210B in the spring semester.  Similarly, a student with strong interests in another discipline, might postpone one of the spring core PhD courses to the second year in order to take a course in that discipline in the first year.  Students who are less mathematically prepared might also be allowed to take the upper division (under-graduate) courses Math 104 and/or 105 in their first year in preparation for 205A and/or 210B in their second year. Students who wish to take this option should consult with their faculty mentor, and then must submit a graduate student petition to the PhD Committee to request permission for  postponement. Such postponement requests will be generally approved for only one course. At all times, students must take four approved graduate courses for a letter grade in their first year.

After the first year: Students with interests primarily in statistics are expected to take at least one semester of each of the core PhD sequences during their studies. Therefore at least one semester (if not both semesters) of the remaining core sequence would normally be completed during the second year. The remaining curriculum for the second and third years would be filled out with further graduate courses in Statistics and with courses from other departments. Students are expected to acquire some experience and proficiency in computing. Students are also expected to attend at least one departmental seminar per week. The precise program of study will be decided in consultation with the student’s faculty mentor.

Remark. Stat 204 is a graduate level probability course that is an alternative to 205AB series that covers probability concepts most commonly found in the applications of probability. It is not taught all years, but does fulfill the requirements of the first year core PhD courses. Students taking Stat 204, who wish to continue in Stat 205B, can do so (after obtaining the approval of the 205B instructor), by taking an intensive one month reading course over winter break.

Designated Emphasis: Students with a Designated Emphasis in Computational and Genomic Biology or Designated Emphasis in Computational and Data Science and Engineering should, like other statistics students, acquire a firm foundation in statistics and probability, with a program of study similar to those above. These programs have additional requirements as well. Interested students should consult with the graduate advisor of these programs. 

Starting in the Fall of 2019, PhD students are required in their first year to take four semesters of the core PhD courses. Students intending to specialize in Probability, however, have the option to substitute an advanced mathematics class for one of these four courses. Such students will thus be required to take Stat 205A/B in the first year,  at least one of Stat 210A/B or Stat 215A/B in the first year, in addition to an advanced mathematics course. This substitute course will be selected in consultation with their faculty mentor, with some possible courses suggested below. Students arriving with advanced coursework equivalent to that of 205AB can obtain permission to substitute in other advanced probability and mathematics coursework during their first year, and should consult with the PhD committee for such a waiver.

During their second and third years, students with a probability focus are expected to take advanced probability courses (e.g., Stat 206 and Stat 260) to fulfill the coursework requirements that follow the first year. Students are also expected to attend at least one departmental seminar per week, usually the probability seminar. If they are not sufficiently familiar with measure theory and functional analysis, then they should take one or both of Math 202A and Math 202B. Other recommended courses from the department of Mathematics or EECS include:

Math 204, 222 (ODE, PDE) Math 205 (Complex Analysis) Math 258 (Classical harmonic analysis) EE 229 (Information Theory and Coding) CS 271 (Randomness and computation)

The Qualifying Examination 

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department.  At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Qualifying Exam Chair. For qualifying exam committees formed in the Fall of 2019 or later, the qualifying exam chair will also serve as the student’s departmental mentor, unless a student already has two thesis advisors. The student must select a qualifying exam chair and obtain their agreement to serve as their qualifying exam chair and faculty mentor. The student's prospective thesis advisor cannot chair the examination committee. Selection of the chair can be done well in advance of the qualifying exam and the rest of the qualifying committee, and because the qualifying exam chair also serves as the student’s departmental mentor (unless the student has co-advisors), the chair is expected to be selected by the beginning of the third year or at the beginning of the semester of the qualifying exam, whichever comes earlier. For more details regarding the selection of the Qualifying Exam Chair, see the "Mentoring" tab.  

Paperwork and Application. Students at the point of taking a qualifying exam are assumed to have already found a thesis advisor and to should have already submitted the internal departmental form to the Graduate Student Services Advisor ( found here ).  Selection of a qualifying exam chair requires that the faculty member formally agree by signing the internal department form ( found here ) and the student must submit this form to the Graduate Student Services Advisor.  In order to apply to take the exam, the student must submit the Application for the Qualifying Exam via CalCentral at least three weeks prior to the exam. If the student passes the exam, they can then officially advance to candidacy for the Ph.D. If the student fails the exam, the committee may vote to allow a second attempt. Regulations of the Graduate Division permit at most two attempts to pass the oral qualifying exam. After passing the exam, the student must submit the Application for Candidacy via CalCentral .

The Doctoral Thesis

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. See Alumni if you would like to view thesis titles of former PhD Students.

Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict. For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website.

Students who have advanced from candidacy (i.e. have taken their qualifying exam and submitted the advancement to candidacy application) must have a joint meeting with their QE chair and their PhD advisor to discuss their thesis progression; if students are co-advised, this should be a joint meeting with their co-advisors. This annual review is required by Graduate Division.  For more information regarding this requirement, please see  https://grad.berkeley.edu/ policy/degrees-policy/#f35- annual-review-of-doctoral- candidates .

Teaching Requirement

For students enrolled in the graduate program before Fall 2016, students are required to serve as a Graduate Student Instructor (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the Fall 2016 entering class, students are required to serve as a GSI for a minimum of two 50% GSI appointment during the regular academic semesters prior to graduation (20 hours a week is equivalent to a 50% GSI appointment for a semester) for Statistics courses numbered 150 and above. Exceptions to this policy are routinely made by the department.

Each spring, the department hosts an annual conference called BSTARS . Both students and industry alliance partners present research in the form of posters and lightning talks. All students in their second year and beyond are required to present a poster at BSTARS each year. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research. It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor (or their faculty mentors if an advisor has not yet been determined).

Mentoring for PhD Students

Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. This faculty mentor at this stage is not expected to be the student’s PhD advisor nor even have research interests that closely align with the student. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships.  Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.

Research-focused mentoring : Once students have found a thesis advisor, that person will naturally be the faculty member most directly overseeing the student’s progression. However, students will also choose an additional faculty member to serve as a the chair of their qualifying exam and who will also serve as a faculty mentor for the student and as a member of his/her thesis committee. (For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor).

The student will be responsible for identifying and asking a faculty member to be the chair of his/her quals committee. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier. Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year (and may have already taken qualifying exams), but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.

Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor. If students are co-advised, this should be a joint meeting with their co-advisors.

If there is a need for a substitute faculty mentor (e.g. existing faculty mentor is on sabbatical or there has been a significant shift in research direction), the student should bring this to the attention of the PhD Committee for assistance.

PhD Student Forms:

Important milestones: .

Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships). 

†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.

Expected Progress Reviews: 

* These meetings do not need to be held in the semester that you take your Qualifying Exam, since the relevant people should be members of your exam committee and will discuss your research progress during your qualifying exam

** If you are being co-advised by someone who is not your primary advisor because your primary advisor cannot be your sole advisor, you should be meeting with that person like a research mentor, if not more frequently, to keep them apprised of your progress. However, if both of your co-advisors are leading your research (perhaps independently) and meeting with you frequently throughout the semester, you do not need to give a fall research progress report.

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Digital Commons @ USF > College of Arts and Sciences > Mathematics and Statistics > Theses and Dissertations

Mathematics and Statistics Theses and Dissertations

Theses/dissertations from 2023 2023.

Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani

Applied Analysis for Learning Architectures , Himanshu Singh

Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze

Theses/Dissertations from 2022 2022

New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana

Advances and Applications of Optimal Polynomial Approximants , Raymond Centner

Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty

On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly

Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He

Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias

Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi

A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman

Theses/Dissertations from 2021 2021

Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri

Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi

Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou

Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando

Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu

Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang

Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang

Theses/Dissertations from 2020 2020

Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi

Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun

Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu

On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman

Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek

Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen

Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan

Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop

On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink

Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang

Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala

Theses/Dissertations from 2019 2019

Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian

Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar

Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil

Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter

Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz

Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi

Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi

Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos

The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva

Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak

Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich

An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado

Power Graphs of Quasigroups , DayVon L. Walker

Theses/Dissertations from 2018 2018

Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed

Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai

A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah

Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa

Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields

Generalizations of Quandles and their cohomologies , Matthew J. Green

Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu

Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou

Human Activity Recognition Based on Transfer Learning , Jinyong Pang

Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham

Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel

Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova

Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang

Theses/Dissertations from 2017 2017

Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack

Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon

On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill

Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill

Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly

Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera

Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao

Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati

Dynamics of Multicultural Social Networks , Kristina B. Hilton

Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi

Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally

Patterns in Words Related to DNA Rearrangements , Lukas Nabergall

Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na

Schreier Graphs of Thompson's Group T , Allen Pennington

Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya

Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo

Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi

Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou

Theses/Dissertations from 2016 2016

A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea

Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery

Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman

On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr

Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim

Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano

Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure

Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru

Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park

Leonard Systems and their Friends , Jonathan Spiewak

Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun

Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu

Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu

Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering , Xing Wang

On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd

Theses/Dissertations from 2015 2015

Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen

Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko

Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana

Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf

Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner

Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao

Theses/Dissertations from 2014 2014

Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo

Recursive Methods in Number Theory, Combinatorial Graph Theory, and Probability , Jonathan Burns

On the Classification of Groups Generated by Automata with 4 States over a 2-Letter Alphabet , Louis Caponi

Statistical Analysis, Modeling, and Algorithms for Pharmaceutical and Cancer Systems , Bong-Jin Choi

Topological Data Analysis of Properties of Four-Regular Rigid Vertex Graphs , Grant Mcneil Conine

Trend Analysis and Modeling of Health and Environmental Data: Joinpoint and Functional Approach , Ram C. Kafle

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PhD Dissertations

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Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses ;
• pass the first-year core program;
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
• satisfy the depth and breadth requirements (2nd/3rd/4th year);
• successfully complete the thesis proposal meeting (winter quarter of the 3rd year);
• present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee 

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member. 

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

 For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

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Watch out for these research topics in Statistics and Big data

Big data is here to stay. But it is no longer a new technology as a lot of firms have already embraced big data; take, for example, Hadoop, which exemplifies an open-source big data project. Think about the following ideas for your next research paper.

• Harness NoSql and Hadoop to accelerate big data processing.
• Swift access to data using in-memory concept.
• Using R programming language for textual data analysis.
• Statistical analyses of psychological dysfunction on pupil’s academic progress.
• Interactive and auto-update of R-plot graphs from a webpage without redrawing.
• Predicting the future using predictive analysis. How big data play a role?
• Smart big data applications to study the past by way of big data.
• Using a circular nonparametric method to estimate entropy.
• Why salaries and expectations of data scientists and data engineers are high?
• Cybersecurity attacks? Merge Hadoop with SIEM (Security Information and Management) application.
• Study on the growth of IoT in various industries and sizable impact of IoT on big data.
• Accelerated Life Testing models: apply stress factors life-testing experiment.
• Extracting hidden patterns and predicting financial markets thru data mining. Using advanced techniques in statistics.
• Using survey sampling tools to understand dyslexia in a specific community.
• Leveraging statistical, computational techniques, neural networks wavelets, and genetic algorithms to solve complex financial issues.
• Decision theoretic method for getting ranking and selection procedures.
• Using advanced mathematical concepts of wavelets in econometric modelling.
• Pharmacodynamics: what are the stochastic models?
• Natural language processing (NLP) in clinical research: Application of methods to anonymize data.
• Correlation between employee engagement and employee performance.
• Analyzing news coverage in politics and identifying patterns.
• Linear method of the equation: compare and contrast of Gaussian elimination and Cholesky decomposition techniques.
• Statistical analysis of criminal offenders.
• Analyzing UK government’s revenue and expenditure.
• Analyzing the trafficking of children and women and negative effects.
• An analysis of the benefits of using information technology in bank services to customers.
• Statistical use of matrices for input and output model and price fixing.
• A statistical evaluation of the road accident rates on a specific period.
• A statistical analysis of reported cases of HIV and STD at a particular period of time.
• A statistical assessment of infant death rates in the state at a certain period of time.
• Statistical regression analysis on country’s GDP – Europe Vs US.
• Health impact of asbestos roof panels: a statistical assessment.
• Statistical study on university pupil’s expenses.
• Statistical analysis on the impact of pesticides on the microflora of soil classifications.
• The contrast on fossil fuels and carbon activated from coconuts—a statistical analysis.
• Critical study on the causes and issues of banking financial distress.
• An analysis of the merits of using financial reports in evaluating bank’s performance.
• Solutions for loan defaults in Indian banks: a detailed analysis.

Other research topics may revolve around Bayesian statistics, matching propensity scores, high-dimensional analysis of data, survival data analyses, and, model selections.

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20 Possible Topics For A PhD Dissertation In Statistics

The dissertation is the culmination of a PhD student's career. Students must pour everything they have learned into their paper. Once completed it should represent of the student's intelligence and academic skills. That's a lot of pressure on one piece of work, with everything this on the line it's easy to run into writer's block. Fortunately, statistics students don't have to go it alone. This article will provide students with 20 statistics topics to inspire creativity.

Twenty Topics to Start With

• Using Predictive Modeling To Prepare for Disease Outbreaks
• Comparing Methods for Generating Probabilistic Forecasts
• A Survey of Modern Linear Models
• Models For Accurately Extrapolating From Historic Population Data
• Analyzing Southeast Asian Typhoons Utilizing Statistical Models
• Using Social Network Information In Epidemiology
• Achieving Optimum Data Density
• How Value-Added Modeling Impacts Test Scores Across Economic Divides
• Looking At Climate Change Through Bayesian Probability
• Accurately Calculating Percentiles
• Modeling Standardized Test Scores Across The Globe
• Using Hierarchical Modeling To Estimate Academic Results
• Monte Carlo Models And Investment Portfolios
• Looking At Health Data Through A Bayesian Lens
• Statistical Analysis Of The Peer Review Process
• Generating Population Structures From Social Network Data
• Accurately Modeling Fragmented Data
• Using Statistical Models To Predict Seasonal Spending
• Minimizing Noise From Automatically Mined Data
• Modeling Wildlife Populations Across Time

Putting these Topics to Use

A few things should stand out after looking at these 20 different titles. The first is that there isn't only one way to go about writing a statistics dissertation. Statisticians can use the tools of their trade to examine just about every aspect of human life. Some of the most productive theses arise from real-world problems. Examining pressing issues using the latest statistical tools is a powerful strategy. This approach allows students to build on something solid, so they don't get too lost in abstract theories. This strategy can also be a good way to prepare for a future career. Students will have the upper hand at companies related to their thesis topic. It's still fine to write about obscure statistical theories. This strategy is a wise choice for academics. Students who aim to join the private market should seek out more practical topics.

Creating Something Bold and New

It's important to remember that these topic ideas are just a starting point. No one should copy these verbatim. Instead, they should act as inspiration. Take them apart, mix them up, add a personal spin, there are a thousand ways to make a topic your own. In the end, a dissertation has to represent the unique skills of the student producing it. In this world, there is nothing new under the sun. Students don't have to reinvent statistics to start off their career. Using ideas like these as a starting point is acceptable. Just make sure you choose a topic that you feel confident about completing.

You might select a bad topic, poorly structure your paper, use inappropriate writing techniques, and so on. Before you start working on your project, you should make sure that each of your step is proper. You can learn everything today.

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Dissertations & Theses

The following is a list of recent statistics and biostatistics PhD Dissertations and Masters Theses.

Jeffrey Gory (2017) PhD Dissertation (Statistics): Marginally Interpretable Generalized Linear Mixed Models Advisors: Peter Craigmile & Steven MacEachern

Yi Lu (2017) PhD Dissertation (Statistics): Function Registration from a Bayesian Perspective Advisors: Radu Herbei & Sebastian Kurtek

Michael Matthews (2017) PhD Dissertation (Statistics):  Extending Ranked Sampling in Inferential Procedures Advisor: Douglas Wolfe

Anna Smith (2017) PhD Dissertation (Statistics):  Statistical Methodology for Multiple Networks Advisor: Catherine Calder

Weiyi Xie (2017) PhD Dissertation (Statistics): A Geometric Approach to Visualization of Variability in Univariate and Multivariate Functional Data Advisor: Sebastian Kurtek

Jingying Zeng (2017) Masters Thesis (Statistics): Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering Advisors: Matthew Pratola & Laura Kubatko

Han Zhang (2017) PhD Dissertation (Statistics): Detecting Rare Haplotype-Environmental Interaction and Nonlinear Effects of Rare Haplotypes using Bayesian LASSO on Quantitative Traits Advisor: Shili Lin

Mark Burch (2016) PhD Dissertation (Biostatistics): Statistical Methods for Network Epidemic Models Advisor: Grzegorz Rempala

Po-hsu Chen (2016) PhD Dissertation (Statistics):  Modeling Multivariate Simulator Outputs with Applications to Prediction and Sequential Pareto Minimization Advisors: Thomas Santner & Angela Dean

Yanan Jia (2016) PhD Dissertation (Statistics): Generalized Bilinear Mixed-Effects Models for Multi-Indexed Multivariate Data Advisor: Catherine Calder

Rong Lu (2016) PhD Dissertation (Biostatistics): Statistical Methods for Functional Genomics Studies Using Observational Data Advisor: Grzegorz Rempala (Public Health)

Junyan Wang (2016) PhD Dissertation (Statistics): Empirical Bayes Model Averaging in the Presence of Model Misfit Advisors: Mario Peruggia & Christopher Hans

Ran Wei (2016) PhD Dissertation (Statistics):  On Estimation Problems in Network Sampling Advisors: David Sivakoff & Elizabeth Stasny

Hui Yang (2016) PhD Dissertation (Statistics):  Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation Advisors: Elizabeth Stasny & Asuman Turkmen

Matthew Brems (2015) Masters Thesis (Statistis): The Rare Disease Assumption: The Good, The Bad, and The Ugly Advisor: Shili Lin

Linchao Chen (2015) PhD Dissertation (Statistics):  Predictive Modeling of Spatio-Temporal Datasets in High Dimensions Advisors: Mark Berliner & Christopher Hans

Casey Davis (2015) PhD Dissertation (Statistics):  A Bayesian Approach to Prediction and Variable Selection Using Nonstationary Gaussian Processes Advisors: Christopher Hans & Thomas Santner

Victor Gendre (2015) Masters Thesis (Statistics): Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency Advisor: Radu Herbei

Zhengyu Hu (2015) PhD Dissertation (Statistics):  Initializing the EM Algorithm for Data Clustering and Sub-population Detection Advisors: Steven MacEachern & Joseph Verducci

David Kline (2015) PhD Dissertation (Biostatistics): Systematically Missing Subject-Level Data in Longitudinal Research Synthesis Advisors: Eloise Kaizar, Rebecca Andridge (Public Health)

Andrew Landgraf (2015) PhD Dissertation (Statistics): Generalized Principal Component Analysis: Dimensionality Reduction through the Projection of Natural Parameters Advisor: Yoonkyung Lee

Andrew Olsen (2015) PhD Dissertation (Statistics):  When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods Advisor: Radu Herbei

Elizabeth   Petraglia (2015) PhD Dissertation (Statistics):  Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS) Advisor: Elizabeth Stasny

Mark   Risser (2015) PhD Dissertation (Statistics):  Spatially-Varying Covariance Functions for Nonstationary Spatial Process Modeling Advisor: Catherine Calder

John Stettler (2015) PhD Dissertation (Statistics):  The Discrete Threshold Regression Model Advisor: Mario Peruggia

Zachary   Thomas (2015) PhD Dissertation (Statistics):  Bayesian Hierarchical Space-Time Clustering Methods Advisor: Mark Berliner

Sivaranjani   Vaidyanathan (2015) PhD Dissertation (Statistics):  Bayesian Models for Computer Model Calibration and Prediction Advisor: Mark Berliner

Xiaomu Wang (2015) PhD Dissertation (Statistics): Robust Bayes in Hierarchical Modeling and Empirical Bayes Analysis in Multivariate Estimation Advisor: Mark Berliner

Staci White (2015) PhD Dissertation (Statistics):  Quantifying Model Error in Bayesian Parameter Estimation Advisor: Radu Herbei

Jiaqi Zaetz (2015) PhD Dissertation (Statistics): A Riemannian Framework for Shape Analysis of Annotated 3D Objects Advisor: Sebastian Kurtek

Fangyuan Zhang (2015) PhD Dissertation (Biostatistics): Detecting genomic imprinting and maternal effects in family-based association studies Advisor: Shili Lin

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120 Statistical Research Topics: Explore Up-to-date Trends

Researchers and statistics teachers are often tasked with writing an article or paper on a given stats project idea. One of the most crucial things in writing an outstanding and well-composed statistics research project, paper, or essay is to come up with a very interesting topic that will captivate your reader’s minds and provoke their thoughts.

What Are the Best Statistical Research Topics Worth Writing On?

Leading statistical research topics for college students that will interest you, project topics in statistics worth considering, the best idea for statistics project you can focus on, good experiments for statistics topics you should be writing on, what are the best ap statistics project ideas that will be of keen interest to you, good statistics project ideas suitable for our modern world, some of the most crucial survey topics for statistics project, statistical projects topics every researcher wants to write on, statistical research topics you can focus your research on.

Students often find it difficult to come up with well-composed statistical research project topics that take the format of argumentative essay topics to pass across their message. In this essay, we will look at some of the most interesting statistics research topics to focus your research on.

Here are some of the best statistical research topics worth writing on:

• Predictive Healthcare Modeling with Machine Learning
• Analyzing Online Education During COVID-19 Epidemic
• Modeling How Climate Change Affects Natural Disasters
• Essential Elements Influencing Personnel Productivity
• Social Media Influence on Customer Choices and Behavior
• Can Geographical Statistics Aid In Analyzing Crime Trends and Patterns?
• Financial Markets and Stock Price Predictions
• Statistical Analysis of Voting-related Behaviors
• An Analysis of Public Transportation Usage Trends in Urban Areas
• How Can Public Health Education Reduce Air Pollution?
• Statistical Analysis of Suicide In Adolescents and Adults
• A Review of Divorce and How It Affects Children

As a college student, here are the best statistical projects for high school students to focus your research on, especially if you need social media research topics .

• Major Factors Influencing College Students’ Academic Performance
• Social Media and How It Defines thee Mental Health of Students
• Evaluation of the Elements Influencing Student Engagement and Retention
• An Examination of Extracurricular Activities On Academic Success
• Does Parental Involvement Determine Academic Achievement of Kids?
• Examining How Technology Affects Improving Educational Performance
• Factors That Motivate Students’ Involvement In Online Learning
• The Impact of Socioeconomic Status On Academic Performance
• Does Criticism Enhance Student Performance?
• Student-Centered Learning and Improved Performance
• A Cursory Look At Students’ Career Goals and Major Life Decisions
• Does Mental Health Impact Academic Achievement?

Are you a student tasked with writing a project but can’t come up with befitting stats research topics? Here are the best ideas for statistical projects worth considering:

• Financial Data And Stock Price Forecasting
• Investigation of Variables Influencing Students’ Grades
• What Causes Traffic Flow and Congestion In Urban Areas?
• How to Guarantee Customer Retention In the Retail Sector
• Using Epidemiological Data to Model the Spread of Infectious Diseases
• Does Direct Advertisement Affect Consumer Preferences and Behavior?
• How to Predict and Adapt to Climate Change
• Using Spatial Statistics to Analyze Trends and Patterns In Crime
• Examination of the Elements Influencing Workplace Morale and Productivity
• Understanding User Behavior and Preferences Through Statistical Analysis of Social Media Data
• How Many Percent Get Married After Their Degree Programs?
• A Comparative Analysis of Different Academic Fee Payments

If you have been confused based on the availability of different statistics project topics to choose from, here are some of the best thesis statement about social media to choose from:

• Analysis of the Variables Affecting A Startup’s Success
• The Valid Connection Between Mental Health and Social Media Use
• Different Teaching Strategies and Academic Performance
• Factors Influencing Employee Satisfaction In Different Work Environments
• The Impact of Public Policy On Different Population Groups
• Reviewing Different Health Outcomes and Incomes
• Different Marketing Tactics for Good Service Promotion
• What Influences Results In Different Sports Competitions?
• Differentiating Elements Affecting Students’ Performance In A Given Subject
• Internal Communication and Building An Effective Workplace
• Does the Use of Business Technologies Boost Workers’ Output?
• The Role of Modern Communication In An Effective Company Management

Are you a student tasked with writing an essay on social issues research topics but having challenges coming up with a topic? Here are some amazing statistical experiments ideas you can center your research on.

• How Global Pandemic Affects Local Businesses
• Investigating the Link Between Income and Health Outcomes In a Demography
• Key Motivators for Student’s Performance In a Particular Academic Program
• Evaluating the Success of a Promotional Plan Over Others
• Continuous Social Media Use and Impact On Mental Health
• Does Culture Impact the Religious Beliefs of Certain Groups?
• Key Indicators of War and How to Manage These Indicators
• An Overview of War As a Money Laundering Scheme
• How Implementations Guarantee Effectiveness of Laws In Rural Areas
• Performance of Students In War-torn Areas
• Key Indicators For Measuring the Success of Your Venture
• How Providing FAQs Can Help a Business Scale

The best AP statistic project ideas every student especially those interested in research topics for STEM students  will want to write in include:

• The Most Affected Age Demography By the Covid-19 Pandemic
• The Health Outcomes Peculiar to a Specific Demography
• Unusual Ways to Enhance Student Performance In a Classroom
• How Marketing Efforts Can Determine Promotional Outputs
• Can Mental Health Solutions Be Provided On Social Media?
• Assessing How Certain Species Are Affected By Climate Change.
• What Influences Voter Turnouts In Different Elections?
• How Many People Have Used Physical Exercises to Improve Mental Health
• How Financial Circumstances Can Determine Criminal Activities
• Ways DUI Laws Can Reduce Road Accidents
• Examining the Connection Between Corruption and Underdevelopment In Africa
• What Key Elements Do Top Global Firms Engage for Success?

If you need some of the best economics research paper topics , here are the best statistics experiment ideas you can write research on:

• Retail Client Behaviors and Weather Trends
• The Impact of Marketing Initiatives On Sales and Customer Retention
• How Socioeconomic Factors Determine Crime Rates In Different Locations
• Public and Private School Students: Who Performs Better?
• How Fitness Affects the Mental Health of People In Different Ages
• Focus On the Unbanked Employees Globally
• Does Getting Involve In a Kid’s Life Make Them Better?
• Dietary Decisions and a Healthy Life
• Managing Diabetes and High Blood Pressure of a Specific Group
• How to Engage Different Learning Methods for Effectiveness
• Understudying the Sleeping Habits of Specific Age Groups
• How the Numbers Can Help You Create a Brand Recognition

As a student who needs fresh ideas relating to the topic for a statistics project to write on, here are crucial survey topics for statistics that will interest you.

• Understanding Consumer Spending and Behavior In Different Regions
• Why Some People in Certain Areas Live Longer than Others
• Comparative Analysis of Different Customer Behaviors
• Do Social Media Businesses Benefit More than Physical Businesses?
• Does a Healthy Work Environment Guarantee Productivity?
• The Impact of Ethnicity and Religion On Voting Patterns
• Does Financial Literacy Guarantee Better Money Management?
• Cultural Identities and Behavioral Patterns
• How Religious Orientation Determines Social Media Use
• The Growing Need for Economists Globally
• Getting Started with Businesses On Social Media
• Which Is Better: A 9-5 or An Entrepreneurial Job?

Do you want to write on unique statistical experiment ideas? Here are some topics you do not want to miss out on:

• Consumer Satisfaction-Related Variables on E-Commerce Websites
• Obesity Rates and Socioeconomic Status In Developed Countries
• How Marketing Strategies Can Make or Mar Sales Performance
• The Correlation Between Increased Income and Happiness In Various Nations
• Regression Models and Forecasting Home Prices
• Climate Change Affecting Agricultural Production In Specific Areas
• A Study of Employee Satisfaction In the Healthcare Industry
• Social Media, Marketing Tactics, and Consumer Behavior In the Fashion Industry
• Predicting the Risk of Default Among Credit Card Holders In Different Regions
• Why Crime Rates Are Increasing In Urban Areas than Rural Areas
• Statistical Evaluation of Methamphetamine’s Impact On Drug Users
• Genes and a Child’s Total Immunity

Here are some of the most carefully selected stat research topics you can focus on.

• Social Media’s Effects On Consumer Behavior
• The Correlation Between Urban Crime Rates and Poverty Levels
• Physical Exercise and Mental Health Consequences
• Predictive Modeling In the Financial Markets
• How Minimum Wage Regulations Impact Employment Rates
• Healthcare Outcomes and Access Across Various Socioeconomic Groups
• How High School Students’ Environment Affect Academic Performance
• Automated Technology and Employment Loss
• Environmental Elements and Their Effects On Public Health
• Various Advertising Tactics and How They Influence Customer Behavior
• Political Polarization And Economic Inequality
• Climate Change and Agricultural Productivity

The above statistics final project examples will stimulate your curiosity and test your abilities, and they can even be linked to some biochemistry topics and anatomy research paper topics . Writing about these statistics project ideas helps provide a deeper grasp of the natural and social phenomena that affect our lives and the environment by studying these subjects.

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression (new 2021-22)
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

PhD in Statistics

Program description.

The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry.  The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas. To enter, students need a bachelor’s degree in mathematics, statistics, or a closely related discipline. Students graduating with a PhD in Statistics are expected to:

• Demonstrate an understanding the core principles of Probability Theory, Estimation Theory, and Statistical Methods.
• Demonstrate the ability to conduct original research in statistics.
• Demonstrate the ability to present research-level statistics in a formal lecture

Requirements for the Ph.D. (Statistics Track)

Course Work A Ph.D. student in our department must complete sixteen courses for the Ph.D. At most, four of these courses may be transferred from another institution. If the Ph.D. student is admitted to the program at the post-Master’s level, then eight courses are usually required.

Qualifying Examinations First, all Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). Then, to qualify a student to begin work on a PhD dissertation, they must pass two of the following three exams at the PhD level: probability, mathematical statistics, and applied statistics. The probability and mathematical statistics exams are offered every September and the applied statistics exam is offered every April.

• PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
• PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
• PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied exam and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.

Note: Students concentrating in probability may choose to do so either through the statistics track or through the mathematics track. If a student wishes to do so through the mathematics track, the course and exam requirements are different. Details are available here .

Dissertation The dissertation is the major requirement for a Ph.D. student. After the student has completed all course work, the Director of Graduate Studies, in consultation with the student, selects a three-member dissertation committee. One member of this committee is designated by the Director of Graduate Studies as the Major Advisor for the student. Once completed, the dissertation must be defended in an oral examination conducted by at least five members of the Department.

The Dissertation and Final Oral Examination follows the   GRS General Requirements for the Doctor of Philosophy Degree .

Satisfactory Progress Toward the Degree Upon entering the graduate program, each student should consult the Director of Graduate Studies (Prof. David Rohrlich) and the Associate Director of the Program in Statistics (Prof. Konstantinos Spiliopoulos). Initially, the Associate Director of the Program in Statistics will serve as the default advisor to the student. Eventually the student’s advisor will be determined in conjunction with their dissertation research. The Associate Director of the Program in Statistics, who will be able to guide the student through the course selection and possible directed study, should be consulted often, as should the Director of Graduate Studies. Indeed, the Department considers it important that each student progress in a timely manner toward the degree. Each M.A. student must have completed the examination by the end of their second year in the program, while a Ph.D. student must have completed the qualifying examination by the third year. Students entering the Ph.D. program with an M.A. degree must have completed the qualifying examination by October of the second year. Failure to meet these deadlines may jeopardize financial aid. Some flexibility in the deadlines is possible upon petition to the graduate committee in cases of inadequate preparation.

Students enrolled in the Graduate School of Arts & Sciences (GRS) are expected to adhere to a number of policies at the university, college, and departmental levels. View the policies on the Academic Bulletin and GRS website .

Residency Post-BA students must complete all of the requirements for a Ph.D. within seven years of enrolling in the program and post-MA students must complete all requirements within five years. This total time limit is set by the Graduate School. Students needing extra time must petition the Graduate School. Also, financial aid is not guaranteed after the student’s fifth year in the program.

Financial Aid

As with all Ph.D. students in the Department of Mathematics and Statistics, the main source of financial aid for graduate students studying statistics is a Teaching Fellowship. These awards carry a stipend as well as tuition remission for six courses per year. Teaching Fellows are required to assist a faculty member who is teaching a course, usually a large lecture section of an introductory statistics course. Generally, the Teaching Fellow is responsible for conducting a number of discussion sections consisting of approximately twenty-five students each, as well as for holding office hours and assisting with grading. The Teaching Fellowship usually entails about twenty hours of work per week. For that reason, Teaching Fellows enroll in at most three courses per semester. A Teaching Fellow Seminar is conducted to help new Teaching Fellows develop as instructors and to promote the continuing development of experienced Teaching Fellows.

Other sources of financial aid include University Fellowships and Research Assistantships. The University Fellowships are one-year awards for outstanding students and are service-free. They carry stipends plus full tuition remission. Students do not need to apply for these fellowships. Research Assistantships are linked to research done with individual faculty, and are paid for through those faculty members’ grants. As a result, except on rare occasions, Research Assistantships typically are awarded to students in their second year and beyond, after student and faculty have had sufficient time to determine mutuality of their research interests.

Regular reviews of the performance of Teaching Fellows and Research Assistants in their duties as well as their course work are conducted by members of the Department’s Graduate Committee.

Ph.D. Program Milestones

The department considers it essential that each student progress in a timely manner toward completion of the degree. The following are the deadlines for achieving the milestones described in the Degree Requirements and constitute the basis for evaluating satisfactory progress towards the Ph.D. These deadlines are not to be construed as expected times to complete the various milestones, but rather as upper bounds. In other words,   a student in good standing expecting to complete   the degree within the five years of guaranteed funding will meet these milestones by the much e arlier target dates indicated below.   Failure to achieve these milestones in a timely manner may affectfinancial aid.

• Target: April of Year 1
• Deadline: April of Year 2
• Target: Spring of Year 2 post-BA/Spring of Year 1 post-MA
• Deadline: End of Year 3 post-BA/Fall of Year 2 post-MA
• Target: Spring of Year 2
• Deadline: End of Year 3
• Target: Spring of Year 2 or Fall of Year 3 post-BA/October of Year 2 post-MA
• Deadline: End of Year 3 post-BA/October of Year 2 post-MA
• Target: end of Year 3
• Deadline: End of Year 4
• Target: End of Year 5
• Deadline: End of Year 6

If you have any questions regarding our PhD program in Statistics, please reach out to us at [email protected]

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Home » 500+ Statistics Research Topics

500+ Statistics Research Topics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data . It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more. As a research topic , statistics can be a fascinating subject to explore, as it allows researchers to investigate patterns, trends, and relationships within data. With the help of statistical methods, researchers can make informed decisions and draw valid conclusions based on empirical evidence. In this post, we will explore some interesting statistics research topics that can be pursued by researchers to further expand our understanding of this field.

Statistics Research Topics

Statistics Research Topics are as follows:

• Analysis of the effectiveness of different marketing strategies on consumer behavior.
• An investigation into the relationship between economic growth and environmental sustainability.
• A study of the effects of social media on mental health and well-being.
• A comparative analysis of the educational outcomes of public and private schools.
• The impact of climate change on agriculture and food security.
• A survey of the prevalence and causes of workplace stress in different industries.
• A statistical analysis of crime rates in urban and rural areas.
• An evaluation of the effectiveness of alternative medicine treatments.
• A study of the relationship between income inequality and health outcomes.
• A comparative analysis of the effectiveness of different weight loss programs.
• An investigation into the factors that affect job satisfaction among employees.
• A statistical analysis of the relationship between poverty and crime.
• A study of the factors that influence the success of small businesses.
• A survey of the prevalence and causes of childhood obesity.
• An evaluation of the effectiveness of drug addiction treatment programs.
• A statistical analysis of the relationship between gender and leadership in organizations.
• A study of the relationship between parental involvement and academic achievement.
• An investigation into the causes and consequences of income inequality.
• A comparative analysis of the effectiveness of different types of therapy for mental health conditions.
• A survey of the prevalence and causes of substance abuse among teenagers.
• An evaluation of the effectiveness of online education compared to traditional classroom learning.
• A statistical analysis of the impact of globalization on different industries.
• A study of the relationship between social media use and political polarization.
• An investigation into the factors that influence customer loyalty in the retail industry.
• A comparative analysis of the effectiveness of different types of advertising.
• A survey of the prevalence and causes of workplace discrimination.
• An evaluation of the effectiveness of different types of employee training programs.
• A statistical analysis of the relationship between air pollution and health outcomes.
• A study of the factors that affect employee turnover rates.
• An investigation into the causes and consequences of income mobility.
• A comparative analysis of the effectiveness of different types of leadership styles.
• A survey of the prevalence and causes of mental health disorders among college students.
• An evaluation of the effectiveness of different types of cancer treatments.
• A statistical analysis of the impact of social media influencers on consumer behavior.
• A study of the factors that influence the adoption of renewable energy sources.
• An investigation into the relationship between alcohol consumption and health outcomes.
• A comparative analysis of the effectiveness of different types of conflict resolution strategies.
• A survey of the prevalence and causes of childhood poverty.
• An evaluation of the effectiveness of different types of diversity training programs.
• A statistical analysis of the relationship between immigration and economic growth.
• A study of the factors that influence customer satisfaction in the service industry.
• An investigation into the causes and consequences of urbanization.
• A comparative analysis of the effectiveness of different types of economic policies.
• A survey of the prevalence and causes of elder abuse.
• An evaluation of the effectiveness of different types of rehabilitation programs for prisoners.
• A statistical analysis of the impact of automation on different industries.
• A study of the factors that influence employee productivity in the workplace.
• An investigation into the causes and consequences of gentrification.
• A comparative analysis of the effectiveness of different types of humanitarian aid.
• A survey of the prevalence and causes of homelessness.
• Exploring the relationship between socioeconomic status and access to healthcare services
• An analysis of the relationship between parental education level and children’s academic performance.
• Exploring the effects of different statistical models on prediction accuracy in machine learning.
• The Impact of Social Media on Consumer Behavior: A Statistical Analysis
• Bayesian hierarchical modeling for network data analysis
• Spatial statistics and modeling for environmental data
• Nonparametric methods for time series analysis
• Bayesian inference for high-dimensional data analysis
• Multivariate analysis for genetic data
• Machine learning methods for predicting financial markets
• Causal inference in observational studies
• Sampling design and estimation for complex surveys
• Robust statistical methods for outlier detection
• Statistical inference for large-scale simulations
• Survival analysis and its applications in medical research
• Mixture models for clustering and classification
• Time-varying coefficient models for longitudinal data
• Multilevel modeling for complex data structures
• Graphical modeling and Bayesian networks
• Experimental design for clinical trials
• Inference for network data using stochastic block models
• Nonlinear regression modeling for data with complex structures
• Statistical learning for social network analysis
• Time series forecasting using deep learning methods
• Model selection and variable importance in high-dimensional data
• Spatial point process modeling for environmental data
• Bayesian spatial modeling for disease mapping
• Functional data analysis for longitudinal studies
• Bayesian network meta-analysis
• Statistical methods for big data analysis
• Mixed-effects models for longitudinal data
• Clustering algorithms for text data
• Bayesian modeling for spatiotemporal data
• Multivariate analysis for ecological data
• Statistical analysis of genomic data
• Bayesian network inference for gene regulatory networks
• Principal component analysis for high-dimensional data
• Time series analysis of financial data
• Multivariate survival analysis for complex outcomes
• Nonparametric estimation of causal effects
• Bayesian network analysis of complex systems
• Statistical inference for multilevel network data
• Generalized linear mixed models for non-normal data
• Bayesian inference for dynamic systems
• Latent variable modeling for categorical data
• Statistical inference for social network data
• Regression models for panel data
• Bayesian spatiotemporal modeling for climate data
• Predictive modeling for customer behavior analysis
• Nonlinear time series analysis for ecological systems
• Statistical modeling for image analysis
• Bayesian hierarchical modeling for longitudinal data
• Network-based clustering for high-dimensional data
• Bayesian spatial modeling for ecological systems.
• Analysis of the Effect of Climate Change on Crop Yields: A Case Study
• Examining the Relationship Between Physical Activity and Mental Health in Young Adults
• A Comparative Study of Crime Rates in Urban and Rural Areas Using Statistical Methods
• Investigating the Effect of Online Learning on Student Performance in Mathematics
• A Statistical Analysis of the Relationship Between Economic Growth and Environmental Sustainability
• Evaluating the Effectiveness of Different Marketing Strategies for E-commerce Businesses
• Identifying the Key Factors Affecting Customer Loyalty in the Hospitality Industry
• An Analysis of the Factors Influencing Student Dropout Rates in Higher Education
• Examining the Impact of Gender on Salary Disparities in the Workplace Using Statistical Methods
• Investigating the Relationship Between Physical Fitness and Academic Performance in High School Students
• Analyzing the Effect of Social Support on Mental Health in Elderly Populations
• A Comparative Study of Different Methods for Forecasting Stock Prices
• Investigating the Effect of Online Reviews on Consumer Purchasing Decisions
• Identifying the Key Factors Affecting Employee Turnover Rates in the Technology Industry
• Analyzing the Effect of Advertising on Brand Awareness and Purchase Intentions
• A Study of the Relationship Between Health Insurance Coverage and Healthcare Utilization
• Examining the Effect of Parental Involvement on Student Achievement in Elementary School
• Investigating the Impact of Social Media on Political Campaigns Using Statistical Methods
• A Comparative Analysis of Different Methods for Detecting Fraud in Financial Transactions
• Analyzing the Relationship Between Entrepreneurial Characteristics and Business Success
• Investigating the Effect of Job Satisfaction on Employee Performance in the Service Industry
• Identifying the Key Factors Affecting the Adoption of Renewable Energy Technologies
• A Study of the Relationship Between Personality Traits and Academic Achievement
• Examining the Impact of Social Media on Body Image and Self-Esteem in Adolescents
• Investigating the Effect of Mobile Advertising on Consumer Behavior
• Analyzing the Relationship Between Healthcare Expenditures and Health Outcomes Using Statistical Methods
• A Comparative Study of Different Methods for Analyzing Customer Satisfaction Data
• Investigating the Impact of Economic Factors on Voter Behavior Using Statistical Methods
• Identifying the Key Factors Affecting Student Retention Rates in Community Colleges
• Analyzing the Relationship Between Workplace Diversity and Organizational Performance
• Investigating the Effect of Gamification on Learning and Motivation in Education
• A Study of the Relationship Between Social Support and Depression in Cancer Patients
• Examining the Impact of Technology on the Travel Industry Using Statistical Methods
• Investigating the Effect of Customer Service Quality on Customer Loyalty in the Retail Industry
• Analyzing the Relationship Between Internet Usage and Social Isolation in Older Adults
• A Comparative Study of Different Methods for Predicting Customer Churn in Telecommunications
• Investigating the Impact of Social Media on Consumer Attitudes Towards Brands Using Statistical Methods
• Identifying the Key Factors Affecting Student Success in Online Learning Environments
• Analyzing the Relationship Between Employee Engagement and Organizational Commitment
• Investigating the Effect of Customer Reviews on Sales in E-commerce Businesses
• A Study of the Relationship Between Political Ideology and Attitudes Towards Climate Change
• Examining the Impact of Technological Innovations on the Manufacturing Industry Using Statistical Methods
• Investigating the Effect of Social Support on Postpartum Depression in New Mothers
• Analyzing the Relationship Between Cultural Intelligence and Cross-Cultural Adaptation
• Investigating the relationship between socioeconomic status and health outcomes using statistical methods.
• Analyzing trends in crime rates and identifying factors that contribute to them using statistical methods.
• Examining the effectiveness of different advertising strategies using statistical analysis of consumer behavior.
• Identifying factors that influence voting behavior and election outcomes using statistical methods.
• Investigating the relationship between employee satisfaction and productivity in the workplace using statistical methods.
• Developing new statistical models to better understand the spread of infectious diseases.
• Analyzing the impact of climate change on global food production using statistical methods.
• Identifying patterns and trends in social media data using statistical methods.
• Investigating the relationship between social networks and mental health using statistical methods.
• Developing new statistical models to predict financial market trends and identify investment opportunities.
• Analyzing the effectiveness of different educational programs and interventions using statistical methods.
• Investigating the impact of environmental factors on public health using statistical methods.
• Developing new statistical models to analyze complex biological systems and identify new drug targets.
• Analyzing trends in consumer spending and identifying factors that influence buying behavior using statistical methods.
• Investigating the relationship between diet and health outcomes using statistical methods.
• Developing new statistical models to analyze gene expression data and identify biomarkers for disease.
• Analyzing patterns in crime data to predict future crime rates and improve law enforcement strategies.
• Investigating the effectiveness of different medical treatments using statistical methods.
• Developing new statistical models to analyze the impact of air pollution on public health.
• Analyzing trends in global migration and identifying factors that influence migration patterns using statistical methods.
• Investigating the impact of automation on the job market using statistical methods.
• Developing new statistical models to analyze climate data and predict future climate trends.
• Analyzing trends in online shopping behavior and identifying factors that influence consumer decisions using statistical methods.
• Investigating the impact of social media on political discourse using statistical methods.
• Developing new statistical models to analyze gene-environment interactions and identify new disease risk factors.
• Analyzing trends in the stock market and identifying factors that influence investment decisions using statistical methods.
• Investigating the impact of early childhood education on long-term academic and social outcomes using statistical methods.
• Developing new statistical models to analyze the relationship between human behavior and the environment.
• Analyzing trends in the use of renewable energy and identifying factors that influence adoption rates using statistical methods.
• Investigating the impact of immigration on labor market outcomes using statistical methods.
• Developing new statistical models to analyze the relationship between social determinants and health outcomes.
• Analyzing patterns in customer churn to predict future customer behavior and improve business strategies.
• Investigating the effectiveness of different marketing strategies using statistical methods.
• Developing new statistical models to analyze the relationship between air pollution and climate change.
• Analyzing trends in global tourism and identifying factors that influence travel behavior using statistical methods.
• Investigating the impact of social media on mental health using statistical methods.
• Developing new statistical models to analyze the impact of transportation on the environment.
• Analyzing trends in global trade and identifying factors that influence trade patterns using statistical methods.
• Investigating the impact of social networks on political participation using statistical methods.
• Developing new statistical models to analyze the relationship between climate change and biodiversity loss.
• Analyzing trends in the use of alternative medicine and identifying factors that influence adoption rates using statistical methods.
• Investigating the impact of technological change on the labor market using statistical methods.
• Developing new statistical models to analyze the impact of climate change on agriculture.
• Investigating the impact of social media on mental health: A longitudinal study.
• A comparison of the effectiveness of different types of teaching methods on student learning outcomes.
• Examining the relationship between sleep duration and productivity among college students.
• A study of the factors that influence employee job satisfaction in the tech industry.
• Analyzing the relationship between income level and health outcomes among low-income populations.
• Investigating the effectiveness of online learning platforms for high school students.
• A study of the factors that contribute to success in online entrepreneurship.
• Analyzing the impact of climate change on agricultural productivity in developing countries.
• A comparison of different statistical models for predicting stock market trends.
• Examining the impact of sports on mental health: A cross-sectional study.
• A study of the factors that influence employee retention in the hospitality industry.
• Analyzing the impact of cultural differences on international business negotiations.
• Investigating the effectiveness of different weight loss interventions for obese individuals.
• A study of the relationship between personality traits and academic achievement.
• Examining the impact of technology on job displacement: A longitudinal study.
• A comparison of the effectiveness of different types of advertising strategies on consumer behavior.
• Analyzing the impact of environmental regulations on corporate profitability.
• Investigating the effectiveness of different types of therapy for treating depression.
• A study of the factors that contribute to success in e-commerce.
• Examining the relationship between social support and mental health in the elderly population.
• A comparison of different statistical methods for analyzing complex survey data.
• Analyzing the impact of employee diversity on organizational performance.
• Investigating the effectiveness of different types of exercise for improving cardiovascular health.
• A study of the relationship between emotional intelligence and job performance.
• Examining the impact of work-life balance on employee well-being.
• A comparison of the effectiveness of different types of financial education programs for low-income populations.
• Analyzing the impact of air pollution on respiratory health in urban areas.
• Investigating the relationship between personality traits and leadership effectiveness.
• A study of the factors that influence consumer behavior in the luxury goods market.
• Examining the impact of social networks on political participation: A cross-sectional study.
• A comparison of different statistical methods for analyzing survival data.
• Analyzing the impact of government policies on income inequality.
• Investigating the effectiveness of different types of counseling for substance abuse.
• A study of the relationship between cultural values and consumer behavior.
• Examining the impact of technology on privacy: A longitudinal study.
• A comparison of the effectiveness of different types of online marketing strategies.
• Analyzing the impact of the gig economy on job satisfaction: A cross-sectional study.
• Investigating the effectiveness of different types of education interventions for improving financial literacy.
• A study of the factors that contribute to success in social entrepreneurship.
• Examining the impact of gender diversity on board performance in publicly-traded companies.
• A comparison of different statistical methods for analyzing panel data.
• Analyzing the impact of employee involvement in decision-making on organizational performance.
• Investigating the effectiveness of different types of treatment for anxiety disorders.
• A study of the relationship between cultural values and entrepreneurial success.
• Examining the impact of technology on the labor market: A longitudinal study.
• A comparison of the effectiveness of different types of direct mail campaigns.
• Analyzing the impact of telecommuting on employee productivity: A cross-sectional study.
• Investigating the effectiveness of different types of retirement planning interventions for low-income individuals.
• Analyzing the effectiveness of different educational interventions in improving student performance
• Investigating the impact of climate change on food production and food security
• Identifying factors that influence employee satisfaction and productivity in the workplace
• Examining the prevalence and causes of mental health disorders in different populations
• Evaluating the effectiveness of different marketing strategies in promoting consumer behavior
• Analyzing the prevalence and consequences of substance abuse in different communities
• Investigating the relationship between social media use and mental health outcomes
• Examining the role of genetics in the development of different diseases
• Identifying factors that contribute to the gender wage gap in different industries
• Analyzing the effectiveness of different policing strategies in reducing crime rates
• Investigating the impact of immigration on economic growth and development
• Examining the prevalence and causes of domestic violence in different populations
• Evaluating the effectiveness of different interventions for treating addiction
• Analyzing the prevalence and impact of childhood obesity on health outcomes
• Investigating the relationship between diet and chronic diseases such as diabetes and heart disease
• Examining the effects of different types of exercise on physical and mental health outcomes
• Identifying factors that influence voter behavior and political participation
• Analyzing the prevalence and impact of sleep disorders on health outcomes
• Investigating the effectiveness of different educational interventions in improving health outcomes
• Examining the impact of environmental pollution on public health outcomes
• Evaluating the effectiveness of different interventions for reducing opioid addiction and overdose rates
• Analyzing the prevalence and causes of homelessness in different communities
• Investigating the relationship between race and health outcomes
• Examining the impact of social support networks on health outcomes
• Identifying factors that contribute to income inequality in different regions
• Analyzing the prevalence and impact of workplace stress on employee health outcomes
• Investigating the relationship between education and income levels in different communities
• Examining the effects of different types of technology on mental health outcomes
• Evaluating the effectiveness of different interventions for reducing healthcare costs
• Analyzing the prevalence and impact of chronic pain on health outcomes
• Investigating the relationship between urbanization and public health outcomes
• Examining the effects of different types of drugs on health outcomes
• Identifying factors that contribute to educational attainment in different populations
• Analyzing the prevalence and causes of food insecurity in different communities
• Investigating the relationship between race and crime rates
• Examining the impact of social media on political participation and engagement
• Evaluating the effectiveness of different interventions for reducing poverty levels
• Analyzing the prevalence and impact of stress on mental health outcomes
• Investigating the relationship between religion and health outcomes
• Examining the effects of different types of parenting styles on child development outcomes
• Identifying factors that contribute to political polarization in different regions
• Analyzing the prevalence and causes of teenage pregnancy in different communities
• Investigating the impact of globalization on economic growth and development
• Examining the prevalence and impact of social isolation on mental health outcomes
• Evaluating the effectiveness of different interventions for reducing gun violence
• Analyzing the prevalence and impact of bullying on mental health outcomes
• Investigating the relationship between immigration and crime rates
• Examining the effects of different types of diets on health outcomes
• Identifying factors that contribute to social inequality in different regions
• Bayesian inference for high-dimensional models
• Analysis of longitudinal data with missing values
• Nonparametric regression with functional predictors
• Estimation and inference for copula models
• Statistical methods for neuroimaging data analysis
• Robust methods for high-dimensional data analysis
• Analysis of spatially correlated data
• Bayesian nonparametric modeling
• Statistical methods for network data
• Optimal experimental design for nonlinear models
• Multivariate time series analysis
• Inference for partially identified models
• Statistical learning for personalized medicine
• Statistical inference for rare events
• High-dimensional mediation analysis
• Analysis of multi-omics data
• Nonparametric regression with mixed types of predictors
• Estimation and inference for graphical models
• Statistical inference for infectious disease dynamics
• Robust methods for high-dimensional covariance matrix estimation
• Analysis of spatio-temporal data
• Bayesian modeling for ecological data
• Multivariate spatial point pattern analysis
• Statistical methods for functional magnetic resonance imaging (fMRI) data
• Nonparametric estimation of conditional distributions
• Statistical methods for spatial econometrics
• Inference for stochastic processes
• Bayesian spatiotemporal modeling
• High-dimensional causal inference
• Analysis of data from complex survey designs
• Bayesian nonparametric survival analysis
• Statistical methods for fMRI connectivity analysis
• Spatial quantile regression
• Statistical modeling for climate data
• Estimation and inference for item response models
• Bayesian model selection and averaging
• High-dimensional principal component analysis
• Analysis of data from clinical trials with noncompliance
• Nonparametric regression with censored data
• Statistical methods for functional data analysis
• Inference for network models
• Bayesian nonparametric clustering
• High-dimensional classification
• Analysis of ecological network data
• Statistical modeling for time-to-event data with multiple events
• Estimation and inference for nonparametric density estimation
• Bayesian nonparametric regression with time-varying coefficients
• Statistical methods for functional magnetic resonance spectroscopy (fMRS) data

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Muhammad Hassan

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Home > Statistics > STATISTICSDISS

Statistics, Department of

Department of statistics: dissertations, theses, and student work.

Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection , Jessica Hauschild

Exploring Experimental Design and Multivariate Analysis Techniques for Evaluating Community Structure of Bacteria in Microbiome Data , Kelsey Karnik

Human Perception of Exponentially Increasing Data Displayed on a Log Scale Evaluated Through Experimental Graphics Tasks , Emily Robinson

Factors Influencing Student Outcomes in a Large, Online Simulation-Based Introductory Statistics Course , Ella M. Burnham

Comparing Machine Learning Techniques with State-of-the-Art Parametric Prediction Models for Predicting Soybean Traits , Susweta Ray

Using Stability to Select a Shrinkage Method , Dean Dustin

Statistical Methodology to Establish a Benchmark for Evaluating Antimicrobial Resistance Genes through Real Time PCR assay , Enakshy Dutta

Group Testing Identification: Objective Functions, Implementation, and Multiplex Assays , Brianna D. Hitt

Community Impact on the Home Advantage within NCAA Men's Basketball , Erin O'Donnell

Optimal Design for a Causal Structure , Zaher Kmail

Role of Misclassification Estimates in Estimating Disease Prevalence and a Non-Linear Approach to Study Synchrony Using Heart Rate Variability in Chickens , Dola Pathak

A Characterization of a Value Added Model and a New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems , Julie M. Garai

Methods to Account for Breed Composition in a Bayesian GWAS Method which Utilizes Haplotype Clusters , Danielle F. Wilson-Wells

Beta-Binomial Kriging: A New Approach to Modeling Spatially Correlated Proportions , Aimee Schwab

Simulations of a New Response-Adaptive Biased Coin Design , Aleksandra Stein

MODELING THE DYNAMIC PROCESSES OF CHALLENGE AND RECOVERY (STRESS AND STRAIN) OVER TIME , Fan Yang

A New Approach to Modeling Multivariate Time Series on Multiple Temporal Scales , Tucker Zeleny

A Reduced Bias Method of Estimating Variance Components in Generalized Linear Mixed Models , Elizabeth A. Claassen

NEW STATISTICAL METHODS FOR ANALYSIS OF HISTORICAL DATA FROM WILDLIFE POPULATIONS , Trevor Hefley

Informative Retesting for Hierarchical Group Testing , Michael S. Black

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[100+] Statistics Research Topics With Free [Thesis Pdf] 2023

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Soft Data and Common Wares Artists-in-Residence

Soft Data and Common Wares is an interdisciplinary collaboration between the DXARTS Softlab (e-textiles and wearable technology lab run by Afroditi Psarra in DXARTS) and Studio Tilt (interaction design research studio run by Audrey Desjardins in Design), that manifests in the form of a print and web publication probing the connections of working with data-driven approaches and crafting physical artifacts, by examining the intimate spaces of the home and the body. Both labs are interested in the process of collecting, archiving, and critically transcoding data from the intimate spaces of the home and the body, and the search for meaningful interpretations. They use material experimentations and prototyping as techniques to shape social identities and create intellectual and cultural commons.

The project includes two artistic residencies that will take place in Spring 2024 between the two labs that will generate a fruitful dialogue, culminating in the collaboration of students, faculty and residents. The two incoming visiting artists that will spend one month each at the DXARTS Softlab and Studio Tilt at the School of Art + Art History + Design are Xiaowei R. Wang - a California-based artist, writer, organizer and coder, and Mark A. Hernandez Motaghy - a Boston-based artist and cultural worker, as well as the co-founding director of Fortunately Magazine. The two artists were selected through an open call which received an astounding 80 proposals from artists around the globe. The goal is to engage the two visiting artists in conversations with DXARTS and Design students that will feed into a new series of zines that will be published in late 2024.

The residencies will center on topics such as: the non-neutrality of data, algorithmic bias, digital labor, the invisible infrastructures of data collection, gender and the body, networks, methods, sonic technologies, interpretative nature of data, and earth + space + body. The residencies will add to the growing collection of zines on the topic of Soft Data and Common Wares . 

Xiaowei R. Wang will work on the project Witch Fever during mid-April to mid-May. Witch Fever is a research based art project at the intersection of climate crisis, colonialism and violent beauty. The project elegantly combines printed textile pieces, speculative botany, textile as archive, colonial histories, and practices of repair. 

Mark A. Hernandez Motaghy will focus on the project Serv/ir from mid-May to mid-June. Serv/ir is a transmedia exploration consisting of video, sculpture, and poetry that creates a diasporic server between Montebello, California, and Colima, Mexico. This work opens questions about family heirlooms, oral histories, diaspora politics, hardware, and craft. 

This residency program is supported by a Kreielsheimer/Jones Grant from the Arts Division of the College of Arts & Sciences.

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