Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 24 for “"Bayesian Variable Selection"”.
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Scalable algorithms for Bayesian variable selection
This Dissertation was approved for publication on 2016-07-14 at 08:43.
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Fast algorithms for Bayesian variable selection
Variable selection of regression and classification models is an important but challenging problem. There are generally two approaches, one based on penalized likelihood, and the other based on Bayesian framework. We focus on the Bayesian framework in which a hierarchical prior is imposed on all …
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Objective bayesian variable selection for censored data
… selecting a set of regressors when the response variable follows a parametric model (such as Weibull or lognormal) and observations are right censored. Under a Bayesian approach, the most widely used tools are the Bayes Factors (BFs) which are, however, undefined when using improper priors. Some …
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Partially Bayesian Variable Selection in Classification Trees
… First, by de-emphasizing certain subsets of variables during the estimation process, unnecessary computational activity can be avoided. Second, by giving an expert's preferred variables priority, we reduce the chance that a spurious variable will appear in the model. Hence, our resulting …
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Bayesian variable selection in high dimensional censored regression models
… development in technologies drives research in variable selection in various fields, especially in bio-medical areas where high-dimensional gene expression data are present. Various approaches have been developed for associating patients' data with patients' survival times, however, not many can …
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Bayesian Variable Selection and Inference for Nonparametric Kernel Machine and Functional Models
… and functional data. In the first study, the Bayesian variable selection method is developed under a generalized fused multi-kernel machine regression. This method can apply to continuous/binary/ordered categorical response variables. We demonstrate the advantage of our method using …
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Some advances in Bayesian variable selection, cognitive diagnostic modeling, and process data analysis
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Bayesian variable selection for linear mixed models when p is much larger than n with applications in genome wide association studies
… this dissertation proposes three different novel Bayesian methods: BICOSS, BGWAS, and IEB. From a Bayesian modeling point of view, SNP search can be seen as a variable selection problem in linear mixed models (LMMs) where $p$ is much larger than $n$. To deal with the $p>>n$ issue, our three …
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Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data
Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of …
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Topics in Bayesian sample size determination and Bayesian model selection.
This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the …
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Bayesian Multilevel-multiclass Graphical Model
… conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed. One is to learn multiple Gaussian graphical models at multilevel from unknown classes. Another one is to select Gaussian process in semiparametric multi-kernel …
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Variable selection for generalized linear mixed models and non-Gaussian Genome-wide associated study data
… of SNPs and n is the sample size, it is a p>>n variable selection problem. To solve this p>>n problem, the common method for GWAS is single marker analysis (SMA). However, since SNPs are highly correlated, SMA identifies true causal SNPs with high false discovery rate. In addition, SMA does not …
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Econometric modelling in a changing, globalised world
This thesis takes the literature on multi-country Bayesian Panel Vector Autoregressions as its starting point. In three self-contained but related essays, we refine and apply the econometric methods and modelling assumptions necessary to objectively consider different aspects of globalisation. The …
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Scalable sparsity structure learning using Bayesian methods
… and theory. In this thesis we develop scalable Bayesian algorithms based on EM algorithm and variational inference to learn sparsity structure in various models. Estimation consistency and selection consistency of our methods are established. First, a nonparametric Bayes estimator is proposed …
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Identifiability for latent class models
… Diagnostic Models (CDMs). CDMs are latent variable models developed to infer latent skills, knowledge, or personalities that underlie responses to educational, psychological, and social science tests and measures. We derive a new set of sufficient conditions for generic identifiability of …
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Bayesian Statistical Methods In Gene-Environment and Gene-Gene Interaction Studies
… capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian …
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Bayesian regularized quantile mixed models for longitudinal studies
… regressions. Here, we aim at developing novel Bayesian regularized quantile mixed effect models to tackle these challenges. In the first project, we have proposed a Bayesian variable selection method in the mixed effect models for longitudinal lipidomics studies. To dissect important …
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An Analytics Approach To Designing Patient Centered Medical Home
… in a structural equation-modeling framework. A Bayesian variable selection with spike and slab prior structure is then developed that allows including or dropping single effects as well as grouped coefficients representing particular model terms. We use a simple parameter expansion to improve …
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Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
<p>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 prior distributions in an objective manner, providing conditions under which …
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ESSAYS ON EMPIRICAL ASSET PRICING USING BAYESIAN METHODS
… We develop a simple multivariate extension of a Bayesian variable selection procedure from the statistics literature to estimate posterior probabilities of asset pricing factors using many assets at once. Using a dataset of thousands of individual stocks in the US market, we calculate posterior …
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