Randomized experiments are a fundamental tool for estimating the causal effects of proposed interventions. While analysis of some experiments can be quite straightforward, other experiments may present difficult analytical ...

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 ...

This thesis is about statistical inference on two classes of data: incomplete data (Chapters 1, 2 and 3) and dependent data (Chapter 4). Chapter 2 relaxes one crucial assumption made in Chapter 1, and then Chapter 3 further ...

In many empirical disciplines, scientific discovery is modularized into discrete papers each investigating one or more hypotheses. Synthesizing these modules of evidence is critical to inform a balanced and appropriately ...

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 ...

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 ...

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 ...

The theory of causal inference, as formalized by the potential outcomes framework, relies on an assumption that the experimental units are independent. When independence is not tenable, we say there is interference, and ...

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. ...

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 ...