Browse Dissertations and Theses - Statistics by Title

  • Noe, Douglas Alan (2006)
    An algorithm that dynamically incorporates expert opinion in this way has two potential advantages, each improving with the quality of the expert. First, by de-emphasizing certain subsets of variables during the estimation ...

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  • Neocleous, Tereza (2005)
    Partly linear models are useful as an extension to linear regression when the response cannot be easily parameterized in terms of all covariates. Their flexibility in keeping some linear terms, while at the same time ...

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  • Mu, Yunming (2005)
    In this thesis, we consider a family of parametric power transformations for the dependent variable such that a linear or partially linear quantile regression model holds after transformation. The two models being considered ...

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  • Hong, Hyokyoung (2008)
    Key words: Ordinal data; Quantile regression; Nonparametric transformation model; Second Longitudinal Study of Aging.

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  • Choi, Seokwoo (2014-09-16)
    Quantile autoregression (QAR) provides an alternative way to study asymmetric dynamics and local persistence in time series. It is particularly attractive for censored data, where the classical autoregressive models are ...

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  • Zhou, Kenneth Qing (1995)
    The thesis consists of six chapters and focus on two topics: quantile regression and survival analysis. Firstly, direct use of regression quantiles to construct confidence intervals and confidence bands for conditional ...

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  • Kim, Mi-Ok (2003)
    Quantile regression extends the statistical quantities of interest beyond conditional means. The regression has been well developed for linear models but less explored for nonparametric models. In this thesis, we consider ...

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  • Lin, Guixian (2009)
    Keywords. Survival Analysis; Quantile Regression; Kaplan-Meier; the Cox Proportional Hazards Model; Accelerated Failure Time Model.

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  • Gao, Yonghong (1999)
    The remainder of my thesis studies multivariate symmetry. Several well-studied parametric and nonparametric tests for univariate symmetry are extended to multivariate settings. We study the asymptotic distributions of these ...

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  • Chang, Yue-Fang (1991)
    Recursive methods for solving the nonparametric regression problem in the GLIMs and computing the Best Linear Unbiased Predictors are discussed here. An iterated state space algorithm is introduced to compute the generalized ...

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  • Xie, Minge (1996)
    The topics of this thesis stem from two EPA/NISS (Environmental Protection Agency/National Institute of Statistical Sciences) projects, which require the use of available data to make risk assessment, estimate uncertainty ...

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  • Zhou, Jianhui (2005)
    The well-known "curse of dimensionality" makes high-dimensional data analysis unusually challenging. Dimension reduction plays a valuable role in enabling certain statistical analyses performed in a parsimonious way. The ...

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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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  • Eisinger, Robert David (2016-07-08)
    We propose new sequential importance sampling methods for sampling contingency tables with fixed margins, loopless, undirected multigraphs, and high-dimensional tables. In each case, the proposals for the method are ...

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  • Chen, Yinghan (2017-06-06)
    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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  • 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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  • Xu, Yi (1995)
    We propose a fully sequential procedure for constructing a fixed width confidence band for an unknown density on a finite interval and show the procedure has the desired coverage probability asymptotically as the width of ...

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  • Kim, Sung Lai (1987)
    In this thesis we study sequential procedures for constructing one-sided and bounded sequential confidence sets with $\beta$-protection and coverage probability at least 1 $-$ $\alpha$ for the mean of a distribution in the ...

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