Browse Dissertations and Theses - Statistics by Contributor "Liang, Feng"

  • Hsu, Ya-Hui (2010-08-20)
    The statistical inference based on the ordinary least squares regression is sub-optimal when the distributions are skewed or when the quantity of interest is the upper or lower tail of the distributions. For example, the ...

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  • Xu, Jianfeng (2011-05-25)
    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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  • Feng, Yang (2011-05-25)
    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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  • Gan, Lingrui (2019-04-19)
    The Bayesian framework offers a flexible tool for regularization in the high dimensional setting. In this thesis, I propose a new class of Bayesian regularization methods induced from scale mixtures of Laplace prior ...

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  • Cui, Na (2012-06-27)
    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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  • Park, Yeon Joo (2017-07-10)
    Functional data arise frequently in numerous scientific fields with the development of modern technology. Accordingly, functional data analysis to extract information on curves or functions is an important area for ...

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  • Huang, Xichen (2017-07-10)
    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 ...

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  • Chen, Gang (2012-06-27)
    Tissue classification and feature selection have been increasing studied during the last two decades, however the available methods are still limited and need improvement. In this manuscript, we develop tissue classification ...

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  • Kim, Ji Young (2010-08-31)
    Data do not always obey the normality assumption, and outliers can have dramatic impacts on the quality of the least squares methods. We use Huber's loss function in developing robust methods for time-course multivariate ...

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  • Wang, Jin (2016-07-14)
    The innovation of modern technologies drives research and development on high-dimensional data analysis in diverse fields, where variable selection plays a pivotal role to ensure credible model estimation. We focus on ...

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  • Ouyang, Yunbo (2018-03-28)
    Learning sparsity pattern in high dimension 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 ...

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  • He, Zhi (2010-08-31)
    Semi-parametric and nonparametric modeling and inference have been widely studied during the last two decades. In this manuscript, we do statistical inference based on semi-parametric and nonparametric models in several ...

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  • Huang, Weihong (2017-06-27)
    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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  • Xia, Jing (2011-05-25)
    Since the early 1990s, functional magnetic resonance imaging (fMRI) has dominated the brain mapping field and it has been proved a powerful tool for mapping human brain functions. The fMRI is a high spatial-temporal ...

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  • Hu, Jianjun (2017-04-20)
    With the fast development of networking, data storage, and the data collection capacity, big data are now rapidly expanding in all science and engineering domains. When dealing with such data, it is appealing if we can ...

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  • Liu, Yufei (2013-08-22)
    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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  • Yang, Ji Yeon (2010-08-31)
    The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational ...

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  • Sewell, Daniel K (2015-04-21)
    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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  • Li, Bin (2013-08-22)
    Nowadays in many statistical applications, we face models whose complexity increases with the sample size. Such models pose a challenge to the traditional statistical analysis, and call for new methodologies and new ...

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  • Gan, Lu (2014-05-30)
    Many variable selection methods are available for linear regression but very little has been developed for quantile regression, especially for the censored problems. This study will look at the possibilities of utilizing ...

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