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Now showing items 11-20 of 212
Bayesian Biclustering on Discrete Data: Variable Selection Methods
(2013-10-18)
Biclustering is a technique for clustering rows and columns of a data matrix simultaneously. Over the past few years, we have seen its applications in biology-related fields, as well as in many data mining projects. As ...
Estimating Individual Causal Effects
(2013-10-18)
Most empirical work focuses on the estimation of average treatment effects (ATE). In this dissertation, I argue for a different way of thinking about causal inference by estimating individual causal effects (ICEs). I ...
Dilemmas in Design: From Neyman and Fisher to 3D Printing
(2014-06-06)
This manuscript addresses three dilemmas in experimental design.
Post-Genomic Approaches to Personalized Medicine: Applications in Exome Sequencing, Microbiome, and COPD
(2014-06-06)
Since the completion of the sequencing of the human genome at the turn of the century, genomics has revolutionized the study of biology and medicine by providing high-throughput and quantitative methods for measuring ...
Topics in Causal Inference and the Law
(2018-04-13)
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 ...
Evaluating Intended and Unintended Consequences of Health Policy and Regulation in Vulnerable Populations
(2013-03-18)
The objective of this dissertation is to evaluate whether two different types of policy interventions in the United States are associated with health service utilization and economic outcomes. Paper 1: The number of ...
Bayesian Methods and Computation for Large Observational Datasets
(2013-09-30)
Much health related research depends heavily on the analysis of a rapidly expanding universe of observational data. A challenge in analysis of such data is the lack of sound statistical methods and tools that can address ...
Partition Models for Variable Selection and Interaction Detection
(2013-09-27)
Variable selection methods play important roles in modeling high-dimensional data and are key to data-driven scientific discoveries. In this thesis, we consider the problem of variable selection with interaction detection. ...
Three Essays of Applied Bayesian Modeling: Financial Return Contagion, Benchmarking Small Area Estimates, and Time-Varying Dependence
(2013-09-27)
This dissertation is composed of three chapters, each an application of Bayesian statistical models to particular research questions. In Chapter 1, we evaluate systemic risk exposure of financial institutions. Building ...
Statistical Learning of Some Complex Systems: From Dynamic Systems to Market Microstructure
(2013-09-27)
A complex system is one with many parts, whose behaviors are strongly dependent on each other. There are two interesting questions about complex systems. One is to understand how to recover the true structure of a complex ...