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Now showing items 11-20 of 24
Regressions for Estimating Main and Principal Causal Effects in Multi-Site Randomized Trials and Small Sample Designs
(2018-05-14)
Randomized controlled experiments have long been considered one of the best settings for evaluating causal impacts for a population of interest. Myriad experimental designs call for different statistical analysis methods. ...
Modeling Musical Influence Through Data
(2018-06-29)
Musical influence is a topic of interest and debate among critics, historians, and general listeners alike, yet to date there has been limited work done to tackle the subject in a quantitative way. In this thesis, we address ...
Robust Methods for Estimating the Intraclass Correlation Coefficient and for Analyzing Recurrent Event Data
(2018-09-25)
Robust statistics have emerged as a family of theories and techniques for estimating parameters of a model while dealing with deviations from idealized assumptions. Examples of deviations include misspecification of ...
Statistical Methods for Data With Latent Structures
(2018-05-11)
This dissertation develops statistical methods to study and utilize the latent structure of data. Here, the latent structure of our interest include but are not limited to latent heterogeneity of rank data, latent seasonal ...
Advances in Monte Carlo Variational Inference and Applied Probabilistic Modeling
(2018-05-10)
Galvanized by the accelerated pace and ease of data collection, researchers in more and more disciplines are turning to large, heterogeneous datasets to answer scientific questions. Divining insight from massive and complex ...
Aspirin and Alcohol in Relation to Lethal Prostate Cancer
(2018-04-26)
Problem: Prostate cancer is the most commonly diagnosed cancer and second-leading cancer cause of death among U.S. men. However, most prostate cancers are indolent. Thus, it is necessary to identify risk factors for lethal ...
Diagnostic Tools in Missing Data and Causal Inference on Time Series
(2018-05-16)
This thesis is divided into two self-contained parts.
The first part focuses on diagnostic tools for missing data. Models for analyzing multivariate data sets with missing values require strong, often unassessable, ...
Detecting Meaningful Relationships in Large Data Sets
(2018-05-01)
As data sets grow and algorithms scale, two questions have become central to data-rich science. The first is the exploration question: how can we avoid only testing hypotheses consistent with current models and instead ...
Statistical Methods for Assessing Complex Multi-Exposure Data in HIV and Genetic Epidemiology
(2018-05-16)
Complex, multi-exposure problems arise in many forms. In this dissertation, we delve into three disparate forms of complex, multi-exposure questions, from the safety of combination antiretroviral (ARV) regimens to the ...
Figure Skating Scores: Prediction and Assessing Bias
(2018-06-29)
Figure skating has not yet benefited from the statistics craze surrounding America's most popular sports. The literature that does analyze figure skating largely deals with the scoring system abandoned in 2004. This thesis ...