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 7 of 7 for “"Kernel Machine Regression"”.
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Bayesian Inference Based on Nonparametric Regression for Highly Correlated and High Dimensional Data
… and high dimensional data. Firstly, group multi-kernel machine regression (GMM) is proposed to identify the association between two sets of multidimensional functions, offering flexibility to effectively capture the complex association among high-dimensional variables. Secondly, semiparametric …
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Bayesian Variable Selection and Inference for Nonparametric Kernel Machine and Functional Models
… 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 bio-photonics Raman spectroscopy to identify which molecular fingerprinting wavenumber is …
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Generalization of kernel machine methods for association testing of multi-omics data
… We perform these analyses using the kernel machine regression (KMR) testing framework. Within this context, we propose three projects. For project one, we extend an existing KMR testing method to accommodate joint association testing of two data types with a trait of interest in …
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Health disparities, environmental toxicants, and midlife women’s health outcomes
… this issue is three-fold; first ordinal logistic regressions were applied to the Midlife Women’s Health Study (MWHS) to understand the relationship between health, demographic, and lifestyle factors on quality of life between racial and minority groups at midlife. Secondly, to understand phthalate …
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Bayesian Multilevel-multiclass Graphical Model
… select Gaussian process in semiparametric multi-kernel machine regression. The first problem is approached by Gaussian graphical model. In this project, I consider learning multiple connected graphs among multilevel variables from unknown classes. I esti- mate the classes of the observations from …
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Semiparametric and Nonparametric Methods for Complex Data
… second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We …
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Early life exposure to metals and adolescent neurodevelopment
… using multivariable linear and logistic regression, generalized estimating equations and multiple informant models. In the third aim, we estimated associations of a metal mixture (lead, manganese, copper, and chromium) with multiple assessments of motor function (N=612). Statistical …