Browsing by Department "Statistical Science"
Now showing items 1-20 of 173
-
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 Black-Scholes-integrated Gaussian Process Model for American Option Pricing
(2020-04-15)Acknowledging the lack of option pricing models that simultaneously have high prediction power, high computational efficiency, and interpretations that abide by financial principles, we suggest a Black-Scholes-integrated ... -
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 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 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 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 ... -
Advances in Survey Methodology and Sports Science
(2019)This thesis develops statistical methodology for efficient uncertainty quantification in the presence of small sample sizes and/or missing data. These methods have a wide range of potential applications, though they are ... -
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 ... -
AN APPLICATION OF GRAPH DIFFUSION FOR GESTURE CLASSIFICATION
(2020)Reliable and widely available robotic prostheses have long been a dream of science fiction writers and researchers alike. The problem of sufficiently generalizable gesture recognition algorithms and technology remains a ... -
An Euler Characteristic Curve Based Representation of 3DShapes in Statistical Analysis
(2021)3D shape analysis is common in many fields such as medical science and biology. Analysis of original shape data is challenging and could be computationally heavy. A simplified representation of 3D shapes could help developing ...
