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 ...

Many traditional, powerhouse sports are currently undergoing an analytics revolution. While ultimate is a relatively young sport, it is certainly not immune to this revolution. Most ultimate data presently track basic ...

Accurate measurement of asset return volatility and correlation is an important problem in financial econometrics. The presence of market microstructure noise in high-frequency data complicates such estimations. This study ...

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, ...

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 ...

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 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 ...

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, ...

Case-noncase studies, also known as case-control studies, are ubiquitous in epidemiology, where a common goal is to estimate the effect of an exposure on an outcome of interest. In many areas of application, such as ...