Browsing by Subject "Statistics"
Now showing items 1-20 of 296
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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 Comparison Of Multiple Imputation Methods For Categorical Data
(2015)This thesis evaluates the performance of several multiple imputation methods for categorical data, including multiple imputation by chained equations using generalized linear models, multiple imputation by chained equations ... -
A Comparison of Serial & Parallel Particle Filters for Time Series Analysis
(2014)This paper discusses the application of parallel programming techniques to the estimation of hidden Markov models via the use of a particle filter. It highlights how the Thrust parallel programming language can be used ... -
A COST-SENSITIVE, SEMI-SUPERVISED, AND ACTIVE LEARNING APPROACH FOR PRIORITY OUTLIER INVESTIGATION
(2023)This master’s thesis presents a novel approach to address the problem of balancing the cost of investigating suspected cases with the potential gain of detecting an outlier, particularly in the context of fraud detection. ... -
A Data-Retaining Framework for Tail Estimation
(2020)Modeling of extreme data often involves thresholding, or retaining only the most extreme observations, in order that the tail may "speak" and not be overwhelmed by the bulk of the data. We describe a transformation-based ... -
A Differentially Private Bayesian Approach to Replication Analysis
(2022)Replication analysis is widely used in many fields of study. Once a research is published, other researchers will conduct analysis to assess the reliability of the published research. However, what if the data are confidential? ... -
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 High-Tech Solution for the Low Resource Setting: A Tool to Support Decision Making for Patients with Traumatic Brain Injury
(2019)Background. The confluence of a capacity-exceeding disease burden and persistent resource shortages have resulted in traumatic brain injury’s (TBI) devastating impact in low and middle income countries (LMIC). Lifesaving ... -
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 ... -
Advanced Topics in Introductory Statistics
(2023)It is now common practice in many scientific disciplines to collect large amounts of experimental or observational data in the course of a research study. The abundance of such data creates a circumstance in which even simply ... -
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 Factor Modeling and Scalable Gaussian Process Regression
(2020)Correlated measurements arise across a diverse array of disciplines such as epidemiology, toxicology, genomics, economics, and meteorology. Factor models describe the association between variables by assuming some latent ... -
Advances in Bayesian Hierarchical Modeling with Tree-based Methods
(2020)Developing flexible tools that apply to datasets with large size and complex structure while providing interpretable outputs is a major goal of modern statistical modeling. A family of models that are especially suitable ... -
Advances in Bayesian Hierarchical Models Motivated by Environmental Applications
(2023)This thesis presents Bayesian hierarchical models that are designed to tackle challenges and accommodate insights from environmental applications. In many environmental applications, we often face high-dimensional and/or ... -
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 ...
