Browsing Colleges and Schools by Department "Statistics"
Now showing items 1-20 of 220
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(2016-07-28)Principle component analysis (PCA) has been a widely used tool for statistics and data analysis for many years. A good result of PCA should be both interpretable and accurate. However, neither interpretability nor accuracy ...
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(2022-07-28)Bayesian optimization (BO), a sequential design strategy for global optimization problem, has gained popularity during last decades for its capability of handling computationally expensive derivative-free objective functions ...
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(2015-08-03)Smoothing splines provide flexible nonparametric regression estimators. Penalized likelihood method is adopted when responses are from exponential families and multivariate models are constructed with certain analysis of ...
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(2011-10-21)The work presented in this dissertation centers on the theme of regression and computation methodology. Functional data is an important class of longitudinal data, and principal component analysis is an important approach ...
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(2012-02-14)Integrated liquid-chromatography mass-spectrometry(LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples.In the last few years,this technology has been used to compare complex ...
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(2018-08-03)The Cox proportional hazards model and the proportional odds model are some of the popular survival models often chosen to analyze censored time-to-event data. The properties of these models have been studied in detail by ...
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(2009-06-02)This dissertation includes two parts. Part 1 develops a geostatistical method to calibrate Texas NexRad rainfall estimates using rain gauge measurements. Part 2 explores the asymptotic joint distribution of sample space-time ...
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(2023-01-06)We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. We model each score component as a smooth term, an extension of ...
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(2022-07-08)Point pattern data are increasingly common in applied research. While these data are inherently locational, social scientists predominately analyze aggregate summaries of these events as areal data. In so doing, they lose ...
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(2012-08-15)Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets ...
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(2021-06-02)Gaussian processes are a powerful and flexible class of nonparametric models that use covariance functions, or kernels, to describe correlations across data. In addition to expressing realistic assumptions, correlation ...
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(Texas A&M University, 2004-11-15)Stochastic compartment models are widely used in modeling processes for biological populations. The residence time has been especially useful in describing the system dynamics in the models. The direct calculation of the ...
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(2022-06-23)We consider the estimation of the marginal likelihood in Bayesian statistics, a essential and important task known to be computationally expensive when the dimension of the parameter space is large. We propose a general ...
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(2020-05-21)Markov chain Monte Carlo (MCMC) sampling methods often do not scale well to large datasets, so there has been an increased interest in approximate Markov chain Monte Carlo (aMCMC) sampling methods. We propose two different ...
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(Texas A&M University, 2005-08-29)Inverse problems arise in many branches of natural science, medicine and engineering involving the recovery of a whole function given only a finite number of noisy measurements on functionals. Such problems are usually ...
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(2012-10-19)The Gaussian geostatistical model has been widely used in Bayesian modeling of spatial data. A core difficulty for this model is at inverting the n x n covariance matrix, where n is a sample size. The computational complexity ...
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(2009-05-15)We propose classification models for binary and multicategory data where the predictor is a random function. The functional predictor could be irregularly and sparsely sampled or characterized by high dimension and sharp ...
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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 ...