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 9 of 9 for “"Spike-and-slab prior"”.
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Using prior-data conflict to tune Bayesian regularized regression models
… becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective methods for variable selection in p > n scenarios provided the shrinkage priors are configured adequately. We …
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Approximate Bayesian approaches and semiparametric methods for handling missing data
… of four research papers focusing on estimation and inference in missing data. In the first paper (Chapter 2), an approximate Bayesian approach is developed to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome …
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Bayesian approaches to time-frequency inverse problems
… or solid, audio signals are better defined and understood by the way their spectral composition evolves over time. Characterising the dynamics of these hidden spectral components—rather than their raw waveform—is crucial in a wide range of practical applications involving sound. However, the …
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Bayesian variable selection in high dimensional censored regression models
… with high-dimensional data while being able to handle censoring. We focus on developing scalable algorithms for variable selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative …
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Scalable sparsity structure learning using Bayesian methods
… is a great challenge in both implementation 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, …
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Bayesian Modeling for Isoform Identification and Phenotype-specific Transcript Assembly
… at the genomic level, transcriptomic level, and proteomic level. Due to the large noise in the data and the high complexity of diseases (such as cancer), it is a challenging task for researchers to extract biologically meaningful information that can help reveal the underlying molecular …
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An Analytics Approach To Designing Patient Centered Medical Home
… Without such balances in clinical supply and demand, issues such as excessive under and over utilization of physicians, long waiting time for receiving the appropriate treatment, and non continuity of care will eliminate many advantages of the medical home strategy. In this research, we …
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Spatial Infectious Disease Transmission Models: Variable Screening Methods and Logistic Formulation.
… affecting not only health, economies, and agriculture, but also global trade, social structures, and education (Rohr et al., 2019; Vurro et al., 2010; Anderson, 2002). Advanced mathematical models of infectious diseases, particularly individual-level models (ILMs), play a crucial role …
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Dimension reduction methods for quantifying local variable importance and the statistical analysis of network data
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01