Covariate Screening in Mixed Linear Models

Spatial Linear Mixed Models with Covariate Measurement Errors.

Spatial data with covariate measurement errors have been commonly observed in public health studies. Existing work mainly concentrates on parameter estimation using Gibbs sampling, and no work has been conducted to understand and quantify the theoretical impact of ignoring measurement error on spatial data analysis in the form of the asymptotic biases in regression coefficients and variance com...

Covariate-Adjusted Generalized Linear Models

We propose covariate adjustment methodology for a situation where one wishes to study the dependency of a generalized response on predictors while both predictors and response are distorted by an observable covariate. The distorting covariate is thought of as a size measurement that affects predictors in a multiplicative fashion. The generalized response is modeled by means of a random threshol...

Testing polynomial covariate effects in linear and generalized linear mixed models.

An important feature of linear mixed models and generalized linear mixed models is that the conditional mean of the response given the random effects, after transformed by a link function, is linearly related to the fixed covariate effects and random effects. Therefore, it is of practical importance to test the adequacy of this assumption, particularly the assumption of linear covariate effects...

Mixed and Covariate Dependent Graphical Models

Mixed and Covariate Dependent Graphical Models by Jie Cheng Co-Chairs: Assoc. Prof. Elizaveta Levina and Prof. Ji Zhu Graphical models have proven to be a useful tool in understanding the conditional dependency structure of multivariate distributions. In Chapters II and III of the thesis, we consider two types of undirected graphical models that are motivated by particular types of applications...

Covariate Model Building in Nonlinear Mixed Effects Models