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  • Unconditional estimating equation approaches for missing data

    Creator:
    Lu, Lin
    Abstract:
    Missing data can lead to biased and inefficient estimation if the missing mechanism is not taken into account in the analysis. In this dissertation we propose two estimators that, under fairly general conditions, are asymptotically unbiased. The first proposed estimator assume the data are missing at random (MAR) and does...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Lin Lu for the degree of Doctor of

  • Nonparametric Estimation of Additive Models with Shape Constraints

    Creator:
    Wang, Lu
    Abstract:
    Monotone additive models are useful in estimating productivity curves or analyzing disease risk where the predictors are known to have monotonic effects on the response. Existing literature mainly focuses on univariate monotone smoothing. Available methods for the estimation of monotone additive models are either difficult to interpret or have no...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Lu Wang for the degree of

  • 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...
    Resource Type:
    Dissertation
    Full Text:
    Proposed Method 50 5.2.2 Basis for Approximating the Distribution of yi given z 51 5.3 Details of

  • Extensions to Gaussian copula models

    Creator:
    Fang, Yan
    Abstract:
    A copula is the representation of a multivariate distribution. Copulas are used to model multivariate data in many fields. Recent developments include copula models for spatial data and for discrete marginals. We will present a new methodological approach for modeling discrete spatial processes and for predicting the process at unobserved...
    Resource Type:
    Dissertation
    Full Text:
    ith observation, Yi, is: p(yi, φ, µi) = Γ(yi + φµi) yi!Γ(φµi) · φ φµi (1 + φ)yi+φµi , where Γ

  • 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...
    Resource Type:
    Dissertation
    Full Text:
    overdispersion are appropriate. If E(Yi) = pi, then some possible models discussed throughout this thesis for

  • Optimization, conservation and valuation of contingent claims in economic resource management under uncertainty

    Creator:
    Jia, Siwei
    Abstract:
    This dissertation consists of three papers studying optimization, conservation and valuation of contingent claims in economic resource nianagement under uncertainty. In the first paper the Markovian optinial policies are studied for resource management in a finite time horizon. Under sonic conditions, in particular, when the prices are stochastic and there...
    Resource Type:
    Dissertation
    Full Text:
    I if Vyi,y2,y3 E I with y1 yi) W(y2) (y1 Y2) (y2)

  • Matrix-Free Methods in Spatial-Temporal Modeling

    Creator:
    Wang, Chunxiao
    Abstract:
    Spatial-temporal data arises in many applications, for example, environment sciences and disease mapping. This dissertation focuses on Gaussian spatial-temporal data. To make statistical inference for Gaussian spatial-temporal data, we developed a special class of spatial-temporal Gaussian state-space models in which the state vectors are constructed following spatial-temporal Gaussian autoregressions that...
    Resource Type:
    Dissertation

  • Conditional expectation given a [sigma]-lattice

    Creator:
    Lee, Chu-in Charles
    Resource Type:
    Dissertation
    Full Text:
    A and (X-X*, Z) = 0 for every Z E S or equivalently, for any Yi E A, (X-X*, Y1 -Z) = 0 for every

  • Local Signal Detection on Irregular Domains with Spatially Varying Coefficient Model

    Creator:
    Zhang, Chengzhu
    Abstract:
    In areas such as spatial analysis and time series analysis, it is essential to understand and quantify spatial or temporal heterogeneity. In this dissertation, we focus on a spatially varying coefficient model, in which spatial heterogeneity is accommodated by allowing the regression coefficients to vary in a given spatial domain....
    Resource Type:
    Dissertation
    Full Text:
    , let Yi be the scalar response and Xi = (Xi1, . . . ,Xid)T be d- dimensional covariates observed at

  • Laplace approximations to likelihood functions for generalized linear mixed models

    Creator:
    Liu, Qing, 1961-
    Abstract:
    This thesis considers likelihood inferences for generalized linear models with additional random effects. The likelihood function involved ordinarily cannot be evaluated in closed form and numerical integration is needed. The theme of the thesis is a closed-form approximation based on Laplace's method. We first consider a special yet important case...
    Resource Type:
    Dissertation
    Full Text:
    mixed model (GLMM). The linear predictor for the ith observation yi is then = b1, (1.1) 2