Browsing by Department "Statistical Science"
Now showing items 1-20 of 135
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A Bayesian Approach to Understanding Music Popularity
(2015-05-08)The Billboard Hot 100 has been the main record chart for popular music in the American music industry since its first official release in 1958. Today, this rank- ing is based upon the frequency of which a song is played ... -
A Bayesian Dirichlet-Multinomial Test for Cross-Group Differences
(2016)Testing for differences within data sets is an important issue across various applications. Our work is primarily motivated by the analysis of microbiomial composition, which has been increasingly relevant and important ... -
A Bayesian Forward Simulation Approach to Establishing a Realistic Prior Model for Complex Geometrical Objects
(2018)Geology is a descriptive science making itself hard to provide quantification. We develop a Bayesian forward simulation approach to formulate a realistic prior model for geological images using the Approximate Bayesian ... -
A Bayesian Model for Nucleosome Positioning Using DNase-seq Data
(2015)As fundamental structural units of the chromatin, nucleosomes are involved in virtually all aspects of genome function. Different methods have been developed to map genome-wide nucleosome positions, including MNase-seq and ... -
A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
(2017)In this paper, we develop an algorithm that utilizes a Bayesian strategy to determine a sequence of questions to play the 20 Question game. The algorithm is motivated with an application to active recommender systems. We ... -
A Geometric Approach for Inference on Graphical Models
(2009)We formulate a novel approach to infer conditional independence models or Markov structure of a multivariate distribution. Specifically, our objective is to place informative prior distributions over graphs (decomposable ... -
A Privacy Preserving Algorithm to Release Sparse High-dimensional Histograms
(2017)Differential privacy (DP) aims to design methods and algorithms that satisfy rigorous notions of privacy while simultaneously providing utility with valid statistical inference. More recently, an emphasis has been placed ... -
A Tapered Pareto-Poisson Model for Extreme Pyroclastic Flows: Application to the Quantification of Volcano Hazards
(2015)This paper intends to discuss the problems of parameter estimation in a proposed tapered Pareto-Poisson model for the assessment of large pyroclastic flows, which are essential in quantifying the size and risk of volcanic ... -
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
(2018)Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the ... -
Advancements in Probabilistic Machine Learning and Causal Inference for Personalized Medicine
(2019)In this dissertation, we present four novel contributions to the field of statistics with the shared goal of personalizing medicine to individual patients. These methods are developed to directly address problems in health ... -
Advances in Bayesian Modeling of Protein Structure Evolution
(2018)This thesis contributes to a statistical modeling framework for protein sequence and structure evolution. An existing Bayesian model for protein structure evolution is extended in two unique ways. Each of these model extensions ... -
Advances in Bayesian Modelling and Computation: Spatio-Temporal Processes, Model Assessment and Adaptive MCMC
(2009)The modelling and analysis of complex stochastic systems with increasingly large data sets, state-spaces and parameters provides major stimulus to research in Bayesian nonparametric methods and Bayesian computation. This ... -
An Analysis of NBA Spatio-Temporal Data
(2017)This project examines the utility of spatio-temporal tracking data from professional basketball games by fitting models predicting whether a player will make a shot. The first part of the project involved the exploration ... -
Applications and Computation of Stateful Polya Trees
(2017)Polya trees are a class of nonparametric priors on distributions which are able to model absolutely continuous distributions directly, rather than modeling a discrete distribution over parameters of a mixing kernel to obtain ... -
Applied Dynamic Factor Analysis for Macroeconomic Forecasting
(2018)The use of dynamic factor analysis in statistical modeling has broad utility across an array of applications. This paper presents a novel hierachical structure suited to a particular class of predictive problems - those ... -
Approximate Bayesian Computation for Complex Dynamic Systems
(2013)This thesis focuses on the development of ABC methods for statistical modeling in complex dynamic systems. Motivated by real applications in biology, I propose computational strategies for Bayesian inference in contexts ... -
Bayesian Adjustment for Multiplicity
(2009)This thesis is about Bayesian approaches for handling multiplicity. It considers three main kinds of multiple-testing scenarios: tests of exchangeable experimental units, tests for variable inclusion in linear regresson ... -
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 efficient ... -
Bayesian Analysis of Latent Threshold Dynamic Models
(Journal of Business and Economic Statistics, 2013-04-01)We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural ... -
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 model ...
