Browsing by Subject "Statistics"
Now showing items 2140 of 180

Bayesian Adjustment for Multiplicity
(2009)This thesis is about Bayesian approaches for handling multiplicity. It considers three main kinds of multipletesting scenarios: tests of exchangeable experimental units, tests for variable inclusion in linear regressonn ... 
Bayesian Analysis and Computational Methods for Dynamic Modeling
(2009)Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally effiicient ... 
Bayesian Analysis of Spatial Point Patterns
(2014)We explore the posterior inference available for Bayesian spatial point process models. In the literature, discussion of such models is usually focused on model fitting and rejecting complete spatial randomness, with moddel ... 
Bayesian and InformationTheoretic Learning of High Dimensional Data
(2012)The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlatted ... 
Bayesian Approaches to File Linking with Faulty Data
(2017)File linking allows analysts to combine information from two or more sources of information, creating linked data bases. From linking school records to track student progress across years, to official statistics and linkking ... 
Bayesian Computation for HighDimensional Continuous & Sparse Count Data
(2018)Probabilistic modeling of multidimensional data is a common problem in practice. When the data is continuous, one common approach is to suppose that the observed data are close to a lowerdimensional smooth manifold. TThere ... 
Bayesian Dynamic Modeling for Streaming Network Data
(2017)Streaming network data of various forms arises in many applications, raising interest in research to model and quantify the nature of stochasticity and structure in dynamics underlying such data. One example context is tthat ... 
Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis
(2016)The advances in three related areas of statespace modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key themme ... 
Bayesian Hierarchical Models for Model Choice
(2013)With the development of modern data collection approaches, researchers may collect hundreds to millions of variables, yet may not need to utilize all explanatory variables available in predictive models. Hence, choosing ... 
Bayesian Hierarchical Models to Address Problems in Neuroscience and Economics
(2017)In the first chapter, motivated by a model used to analyze spike train data, we present a method for learning multiple probability vectors by using information from large samples to improve estimates for smaller samples.. ... 
Bayesian Inference in Largescale Problems
(2016)Many modern applications fall into the category of "largescale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on pooint ... 
Bayesian Inference Via Partitioning Under Differential Privacy
(2018)In this thesis, I develop differentially private methods to report posterior probabilities and posterior quantiles of linear regression coefficients. I accomplish this by randomly partitioning the data, taking an intermeediate ... 
Bayesian Kernel Models for Statistical Genetics and Cancer Genomics
(2017)The main contribution of this thesis is to examine the utility of kernel regression ap proaches and variance component models for solving complex problems in statistical genetics and molecular biology. Many of these typpes ... 
Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas
(2016)Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hhidden ... 
Bayesian metaanalysis models for heterogeneous genomics data
(2013)The accumulation of highthroughput data from vast sources has drawn a lot attentions to develop methods for extracting meaningful information out of the massive data. More interesting questions arise from how to combinee ... 
Bayesian Methods for TwoSample Comparison
(2015)Twosample comparison is a fundamental problem in statistics. Given two samples of data, the interest lies in understanding whether the two samples were generated by the same distribution or not. Traditional twwosample ... 
Bayesian Methods to Characterize Uncertainty in Predictive Modeling of the Effect of Urbanization on Aquatic Ecosystems
(2010)Urbanization causes myriad changes in watershed processes, ultimately disrupting the structure and function of stream ecosystems. Urban development introduces contaminants (human waste, pesticides, industrial chemicals)). ... 
Bayesian Mixture Modeling Approaches for Intermediate Variables and Causal Inference
(2010)This thesis examines causal inference related topics involving intermediate variables, and uses Bayesian methodologies to advance analysis capabilities in these areas. First, joint modeling of outcome variables with inteermediate ... 
Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
(2010)The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prioor ... 
Bayesian Modeling and Adaptive Monte Carlo with Geophysics Applications
(2013)The first part of the thesis focuses on the development of Bayesian modeling motivated by geophysics applications. In Chapter 2, we model the frequency of pyroclastic flows collected from the Soufriere Hills volcano. Mulltiple ...