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Browse Dissertations and Theses  Statistics by Contributor "Chen, Yuguo"
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(20120201)Bayesian inference provides a flexible way of combiningg data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression ...
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(20110525)The latent class model (LCM) is a statistical method that introduces a set of latent categorical variables. The main advantage of LCM is that conditional on latent variables, the manifest variables are mutually independent ...
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(20110525)Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is desired. The traditional frequentists’ approach ...
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(20170630)Over the last two decades, we have witnessed a massive explosion of our data collection abilities and the birth of a "big data" age. This has led to an enormous interest in statistical inference of a new type of complex ...
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(20120627)For my thesis, I have worked on two projects: modeling parasite dynamics (Chapter 2) and complementary dimensionality analysis (Chapter 3). In the first project, we study a longitudinal data of infection with the parasite ...
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(20120918)The state space model has been widely used in various fields including economics, finance, bioinformatics, oceanography, and tomography. The goal of the filtering problem is to find the posterior distribution of the hidden ...
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(20181204)In part 1, we propose a pointwise inference algorithm for highdimensional linear models with timevarying coefficients and dependent error processes. The method is based on a novel combination of the nonparametric kernel ...
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Sampling for conditional inference on contingency tables, multigraphs, and high dimensional tables (20160708)We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and highdimensional tables. In each case, the proposals for the method are ...
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(20170606)Monte Carlo methods provide tools to conduct statistical inference on models that are difficult or impossible to compute analytically and are widely used in many areas of statistical applications, such as bioinformatics ...
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(20170627)Computational statistics, including methods such as Markov chain Monte Carlo (MCMC), bootstrap, approximate Bayesian computation, is an important part in modern statistics and has been widely used in many areas, such as ...
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(20160708)This dissertation is divided into two parts, concerning two areas of statistical methodology. The first part of this dissertation concerns statistical analysis of networks with community structure. The second part of this ...
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(20190419)With the growing awareness of the potential for microbial communities to play a role in human health, environmental remediation and other important processes, the challenge of understanding such a complex population through ...
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(20180417)This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven OrnsteinUhlenbeck processes), the transition density may ...
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(20130822)Functional data Analysis has emerged as an important area of statistics which provides convenient and informative tool for the analysis of data objects of high dimension/high resolution. In the literature, it seems that ...
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(20140530)Networks arise from modeling complex systems in various fields, such as computer science, social science, biology, psychology and finance. Understanding and analyzing networks help us better understand these complex systems ...
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(20191203)We propose new methods and frameworks for approaching three different statistical sequence problems. The first is a treebased computational method for calculating the Poisson Binomial distribution function, which is the ...
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(20130822)This dissertation is centered on the modeling of heterogeneous data which is ubiquitous in this digital information age. From the statistical point of view heterogeneous data is composed of dissimilar components, where ...
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(20150421)Dyadic data are ubiquitous and arise in the fields of biology, epidemiology, sociology, and many more. Such dyadic data are often best understood within the framework of networks. Network data can vary in many ways. For ...
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(20190419)Monte Carlo methods are widely used in statistical computing area to solve different problems. Social network analysis plays an importance role in many fields. In this dissertation, we focus on improving the efficiency of ...
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Now showing items 119 of 19