Students who write theses in statistics spend an entire academic year working on an in-depth study with the help and direction of a faculty member. In addition to writing a thesis, each student gives a seminar late in spring semester. The thesis seminar weeks are busy and exciting times around the department.

Copies of some past student theses are located in the Seeley Mudd library.

2024

  • Elizabeth Zhang worked with Prof. Amy Wagaman on "Flexible Bayesian regression models for complex interactions in exposure mixture studies"
  • Justin Papagelis worked with Prof. Shu-Min Liao on "On the Analysis of Likert-Scale Data: Historical Debates and Newly Developed Approaches"
  • Dasha Asienga worked with Prof. Kat Correia on "Algorithmic Bias, Statistical Notions of Fairness, and the Seldonian Framework"

2023

  • Kevin Jin worked with Prof. Shu-Min Liao on "Visualizing Simpson's Paradox in High-Dimensional Contingency Tables using Checkerboard Copula Regression"
  • Clara Page worked with Prof.  Kat Correia on "Assessing the Robustness of Inverse Probability of Censoring Weighting"
  • Michael Pitts worked with Prof. Nick Horton on "Impact of Missing Data on Clinical Trials: A Sensitivity Analysis Approach using Pattern-Mixture Models"

2022

  • Kenny Chen worked with Prof. Shu-Min Liao on "A model-free dependence measure for high-dimensional contingency tables and its potential as a goodness of fit measure"
  • Andrej Pospisil worked with Prof. Amy Wagaman on "Evaluation of effectiveness of predictive mean matching in binary classification"
  • Alex Ristic worked with Prof. Ryan McShane on "Engineering Social Network Features to Model a Stock Volatility Time Series"
  • Cat Sarosi worked with Prof. Amy Wagaman on "Item Response Theory Models for the Quantitative Reasoning for College Science Assessment"
  • Jessica Yu worked with Prof. Brittney Bailey on "Robustness of Degrees of Freedom Approximation in Linear Mixed Models with Nonnormal Distributions"

2021

  • Andrea Boskovic worked with Prof. Amy Wagaman on "Imbalanced Data Classification with Neural Networks and Classifiers"
  • Jasper Flint worked with Prof. Amy Wagaman on "Comparison of Image Feature Extraction and Classification Methods"
  • Konstantin Larin worked with Prof. Amy Wagaman on "A Comparison of Effectiveness of Different Network Sampling Algorithms in Estimating Network Statistics"
  • Tony Ni worked with Prof. Brittney Bailey on "Evaluation of Parameter Estimation Methods to Handle Left-Censored Missingness"
  • Breanna Richards worked with Prof. Katharine Correia on "Classification of Lymphatic Cancer Types Using Random Forests and LASSO Regression"

2020

  • Margaret Chien worked with Prof. Katharine Correia on "Modeling Fatalities in Armed Conflict with Mixed Effects Models"
  • Dahyun Jessica Jeong worked with Prof. Nicholas Horton on "Scaling Statistical Modeling and Analysis for Larger Datasets Using Sparklyr"

2019

  • Zachary Brown worked with Prof. Nicholas Horton on "An EM Approach to Solving Coarsened-Data Problems Caused by Lower Limits of Detection"
  • Fengling Hu worked with Prof. Amy Wagaman on "Bayesian Inference and One-Shot Learning"
  • Robert Zielinski worked with Prof. Amy Wagaman on "A Comparison of Bayesian Inference Algorithms for Hierarchical Mixture Models"

2018

  • Jonathan Che worked with Prof. Albert Kim on “Resampling Methods for Model Assessment and Selection with Extensions to Spatial Data”
  • Brendan Seto worked with Prof. Nicholas Horton on “Causal Inference”
  • Sarah Teichman worked with Prof. Amy Wagaman on “The Impact of Different Edge Weightings on Community Detection in Social Networks”

2017

  • Caleb Ki worked with Prof. Nicholas Horton on "Missing Data in Randomized Clinical Trials"
  • Levi Lee worked with Prof. Amy Wagaman on "A Simulation Study Using Random Graph Models to Fit Social Networks" (technically, a thesis in mathematics)