This dissertation develops and applies several statistical and computational methods to the analysis of Next Generation Sequencing (NGS) data in order to gain a better understanding of our biology. In the rest of the chapter ...

This manuscript addresses two topics in Bayesian inference for causal effects. 1) Treatment noncompliance is frequent in clinical trials, and because the treatment actually received may be different from that assigned, ...

This dissertation studies the problem of causal inference for ordinal outcomes. Chapter 1 focuses on the sharp null hypothesis of no treatment effect on all experimental units, and develops a systematic procedure for ...

Chapter 1: Since the early 2000s, evidence has accumulated for a significant differential effect of first-line antiretroviral therapy (ART) regimens on human immunodeficiency virus (HIV) treatment outcomes, such as CD4 ...

In chapter one a Liability Threshold Mixed Linear Model (LTMLM) association statistic is introduced for ascertained case-control studies that increases power vs. existing mixed model methods for diseases with low prevalence, ...

The first part of this thesis introduces a new statistical method to estimate parameter values in a mixed population consisting of both single- and bi- phasic longitudinal trajectories. This pro- posed model is capable of ...

How to properly aggregate indirect information is more and more important. In this dissertation, we will present two aspects of the issue: indirect comparison of treatment effects and aggregation of ordered-based rank data.

Core-collapse supernovae (SNe) are the highly energetic explosions of massive stars (> 8 solar masses) that are pervasive in their influence throughout astrophysics. They are the phenomenon with primary responsibility for ...

The de-identification of publicly released datasets that contain personal information is necessary to preserve personal privacy. One such de-identification algorithm, k-anonymization, reduces the risk of the re-identification ...

Differential privacy is one recent framework for analyzing and quantifying the amount of privacy lost when data is released. Meanwhile, multiple imputation is an existing Bayesian-inference based technique from statistics ...