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  • The laplace approximation and inference in generalized linear models with two or more random effects

    Creator:
    Pratt, James L.
    Abstract:
    This thesis proposes an approximate maximum likelihood estimator and likelihood ratio test for parameters in a generalized linear model when two or more random effects are present. Substantial progress in parameter estimation for such models has been made with methods involving generalized least squares based on the approximate marginal mean...
    Resource Type:
    Dissertation
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    niel Schafer This thesis proposes an approximate maximum likelihood estimator and likelihood ratio

  • Generalized linear mixed models with censored covariates

    Creator:
    Giovanini, John
    Abstract:
    This dissertation is about statistical methods for data analysis using generalized linear mixed models (GLMMs) with censored covariates. Special attention in given to the particular problem of inference about age-specific reproductive success in wild animal populations using some animals with known ages and some animals with ages only known to...
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    Dissertation
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    : Generalized Linear Mixed Models with Censored Covariates Abstract approved: Daniel W. Schafer

  • Likelihood analysis of the multivariate ordinal probit model for repeated and spatial ordered categorical responses

    Creator:
    Li, Yonghai
    Abstract:
    This dissertation is about the likelihood analysis of ordered categorical responses in a longitudinal/spatial study, meaning regression-like analysis when the response variable is categorical with ordered categories, and is measured repeatedly over time or space on the experimental or sampling units. Particular attention is given to the multivariate ordinal probit...
    Resource Type:
    Dissertation
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    : __________________________ Daniel W. Schafer This dissertation is about the

  • Regression calibration and maximum likelihood inference for measurement error models

    Creator:
    Monleon-Moscardo, Vicente J.
    Abstract:
    Regression calibration inference seeks to estimate regression models with measurement error in explanatory variables by replacing the mismeasured variable by its conditional expectation, given a surrogate variable, in an estimation procedure that would have been used if the true variable were available. This study examines the effect of the uncertainty...
    Resource Type:
    Dissertation
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    Models Abstract approved: Daniel W. Schafer Regression calibration inference seeks to

  • Semiparametric maximum likelihood for regression with measurement error

    Creator:
    Suh, Eun-Young
    Abstract:
    Semiparametric maximum likelihood analysis allows inference in errors-invariables models with small loss of efficiency relative to full likelihood analysis but with significantly weakened assumptions. In addition, since no distributional assumptions are made for the nuisance parameters, the analysis more nearly parallels that for usual regression. These highly desirable features and...
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    Dissertation
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    Measurement Error. Abstract approved: Daniel W. Schafer Semiparametric maximum likelihood analysis allows

  • Application and computation of likelihood methods for regression with measurement error

    Creator:
    Higdon, Roger
    Abstract:
    This thesis advocates the use of maximum likelihood analysis for generalized regression models with measurement error in a single explanatory variable. This will be done first by presenting a computational algorithm and the numerical details for carrying out this algorithm on a wide variety of models. The computational methods will...
    Resource Type:
    Dissertation
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    Methods for Regression with Measurement Error. Abstract approved: Daniel W. Schafer This thesis

  • Extensions for paired comparisons models

    Creator:
    Kolsky, James D.
    Abstract:
    The Thurstone-Mosteller and Bradley-Terry Models are commonly used to rank items from paired comparisons experiments in which one item in each pair "wins," and to assess the importance of time-independent explanatory variables on such rankings. The first part of this thesis clarifies the use of probit and logistic regression models...
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    Dissertation
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    Models. Abstract approved: Daniel W. Schafer The Thurstone-Mosteller and Bradley-Terry Models are

  • Diagnostic tools for overdispersion in generalized linear models

    Creator:
    Ganio-Gibbons, Lisa M.
    Abstract:
    Data in the form of counts or proportions often exhibit more variability than that predicted by a Poisson or binomial distribution. Many different models have been proposed to account for extra-Poisson or extra-binomial variation. A simple model includes a single heterogeneity factor (dispersion parameter) in the variance. Other models that...
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    Dissertation
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    Models Abstract approved:Redacted for Privacy Daniel W. Schafer() Data in the form of counts or

  • A method for estimation of generalized linear models when explanatory variables contain meaurement error

    Creator:
    Sauter, Roger M.
    Abstract:
    This thesis considers the problem of estimating the linear parameters of generalized linear models (GLM), especially binomial and Poisson regression models, when the explanatory variable is subject to measurement error. In this situation, the dependence of the response variable on the observed explanatory variable cannot typically be modeled as a...
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    Dissertation
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    approved: Redacted for Privacy Daniel W. Schafer This thesis considers the problem of estimating the

  • Statistical analysis of RNA sequencing count data

    Creator:
    Mi, Gu
    Abstract:
    RNA-Sequencing (RNA-Seq) has rapidly become the de facto technique in transcriptome studies. However, established statistical methods for analyzing experimental and observational microarray studies need to be revised or completely re-invented to accommodate RNA-Seq data's unique characteristics. In this dissertation, we focus on statistical analyses performed at two particular stages in...
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    Dissertation
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    Sequencing Count Data Abstract approved: Yanming Di Daniel W. Schafer RNA-Sequencing (RNA-Seq) has rapidly