Browsing by Department "Statistics"
Now showing items 21-40 of 234
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(2019-07-25)Estimation of correlation matrices is a challenging problem due to the notorious positive-definiteness constraint and high-dimensionality. Reparameterising Cholesky factors of correlation matrices in terms of angles or ...
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(2012-10-19)We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision matrix, and thus conduct model selection and ...
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(2021-04-12)This dissertation explores various applications of Bayesian hierarchical modeling to accommodate general characteristics of the modern data: complex data structure, high-dimensionality, and huge data size. The focus of the ...
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(2011-10-21)Global marketing managers are keenly interested in being able to predict the sales of their new products. Understanding how a product is adopted over time allows the managers to optimally allocate their resources. With ...
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(Texas A&M University, 2006-08-16)Mixture models are useful in describing a wide variety of random phenomena because of their flexibility in modeling. They have continued to receive increasing attention over the years from both a practical and theoretical ...
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(2012-10-19)We present techniques for joint modeling of binomial and rank response data using the Bayesian paradigm for inference. The motivating application consists of results from a series of assessments on several primate species. ...
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(2009-05-15)Life sciences research is advancing in breadth and scope, affecting many areas of life including medical care and government policy. The field of Bioinformatics, in particular, is growing very rapidly with the help of ...
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(2012-08-17)Record matching is a fundamental and ubiquitous part of today?s society. Anything from typing in a password in order to access your email to connecting existing health records in California with new health records in New ...
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(Texas A&M University, 2007-04-25)This work is directed towards developing flexible Bayesian statistical methods in the semi- and nonparamteric regression modeling framework with special focus on analyzing data from biological and genetic experiments. This ...
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(2012-02-14)This dissertation has mainly two parts. In the first part, we propose a bivariate nonlinear multivariate measurement error model to understand the distribution of dietary intake and extend it to a multivariate model to ...
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(2023-03-30)In a regression setup, one of the most encountered problem is the presence of error in the predictor(s) - also known as measurement error models. A popular way of dealing with measurement error models is a semiparametric ...
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(2012-07-16)Bayesian methods are often criticized on the grounds of subjectivity. Furthermore, misspecified priors can have a deleterious effect on Bayesian inference. Noting that model selection is effectively a test of many hypotheses, ...
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(Texas A&M University, 2004-11-15)This research consists of two parts. The first part examines the posterior probability integrals for a family of linear models which arises from the work of Hart, Koen and Lombard (2003). Applying Laplace's method ...
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(Texas A&M University, 2005-08-29)Bioinformatics applications can address the transfer of information at several stages of the central dogma of molecular biology, including transcription and translation. This dissertation focuses on using Bayesian models ...
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(Texas A&M University, 2005-08-29)Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a ...
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(2022-02-10)Public opinion polling data has unique features that complicate statistical inference. Polling data is often non-representative of the population it aims to estimate. It is also common for individual polls to have a large ...
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(Texas A&M University, 2004-11-15)Univariate hierarchical Bayes models are being vigorously researched for use in disease mapping, engineering, geology, and ecology. This dissertation shows how the models can also be used to build modelbased risk maps for ...
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(2011-10-21)The protein structure prediction problem consists of determining a protein’s three-dimensional structure from the underlying sequence of amino acids. A standard approach for predicting such structures is to conduct a ...
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(2021-06-29)This dissertation focuses on Bayesian semiparametric regression techniques under constrained setting, and its methodological, computational and applied aspects. We study several non-traditional statistical problems that ...
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Bayesian Semiparametric Density Deconvolution and Regression in the Presence of Measurement Errors (2014-06-24)Although the literature on measurement error problems is quite extensive, solutions to even the most fundamental measurement error problems like density deconvolution and regression with errors-in-covariates are available ...