Browse Graduate Dissertations and Theses at Illinois by Contributor "Qu, Annie"

  • 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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  • Bi, Xuan (2016-06-14)
    Large-scale complex data have drawn great attention in recent years, which play an important role in information technology and biomedical research. In this thesis, we address three challenging issues: sufficient dimension ...

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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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  • Zhu, Xiaolu (2017-05-31)
    Personalization has broad applications in many fields these days. Due to significant subject variations, it has become critical to incorporate subjects' heterogeneous characteristics in order to efficiently allocate ...

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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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  • Tang, Xiwei (2017-07-13)
    Individualized modeling and multi-modality data integration have experienced an explosive growth in recent years, which have many important applications in biomedical research, personalized education and marketing. ...

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  • He, Yifeng (2018-12-04)
    In part 1, we propose a pointwise inference algorithm for high-dimensional linear models with time-varying coefficients and dependent error processes. The method is based on a novel combination of the nonparametric kernel ...

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  • Rho, Yeonwoo (2014-09-16)
    In this thesis we develop inferential methods for time series models with weakly dependent errors in the following three aspects. The first aspect concerns the issue of the size-distortion in the presence of strong ...

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  • Kinson, Christopher Leron (2017-07-12)
    Large-scale data or big data is an enormously popular word in the data science and statistics communities. These datasets are often collected over periods of time - at hourly and weekly rates - with the help of technological ...

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  • Wang, Peng (2011-08-25)
    Longitudinal data arise frequently in many studies where measurements are obtained from a subject repeatedly over time. Consequently, measurements within a subject are correlated. We address two rather important but ...

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  • Cho, Hyun Keun (2013-08-22)
    High-dimensional correlated data arise frequently in many studies. My primary research interests lie broadly in statistical methodology for correlated data such as longitudinal data and panel data. In this thesis, we address ...

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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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  • Zhang, Xianyang (2013-08-22)
    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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  • Fan, Zhewen (2011-01-14)
    Chapter 1 is concerned with confidence interval construction for the mean of a long-range dependent time series. It is well known that the moving block bootstrap method produces an inconsistent estimator of the distribution ...

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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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  • Shu, Xinxin (2014-09-16)
    This thesis contains two research areas including time-varying networks estimation and Chinese words segmentation. Chapter 1 introduces the background of the time-varying networks and the structure of Chinese language, ...

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  • Shi, Peibei (2015-07-16)
    Weak signal identification and inference are very important in the area of penalized model selection, yet they are under-developed and not well-studied. Existing inference procedures for penalized estimators are mainly ...

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