In the first part of the dissertation, we derive two methods for responders analysis in longitudinal data with random missing data. Often a binary variable is generated by dichotomizing an underlying continuous variable measured at a specific point in time according to a prespecified threshold...

In much of applied statistics variables of interest are measured with error. In particular, regression with covariates that are subject to measurement error requires adjustment to avoid biased estimates and invalid inference. We consider two aspects of this problem. Detection Limits (DL) arise...

In many clinical studies, researchers are mainly interested in studying the effects of some prognostic factors on the hazard of failure from a specific cause while individuals may failure from multiple causes. This leads to a competing risks problem. Often, due to various reasons such as finite...

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of...

This dissertation focuses on two topics in semiparametric statistical methods and their applications in medical science: (1) prediction of patients? lifetimes based on their risk profiles; (2) estimation of dynamic exposure effects on survival outcomes. In Chapter II, we develop multiple...

This thesis is comprised of three chapters. The first chapter develops a statistical model that enriches the role that firms playin wage determination, allowing firms to influence both average wages as well as the returns to observable worker characteristics. I exploit the hierarchical nature of...

Positive and negative predictive values are important measures of accuracy whenone compares the accuracy of diagnostic tests. When more than one diagnostic tests areavailable, one may has to choose one of the possible diagnostic tests due to cost, time, orethical reason. We consider a pair study...

Considerable recent interest has focused on doubly robust estimatorsfor a population mean response in the presence of incomplete data,which involve models for both the propensity score and the regressionof outcome on covariates. The ``usual" doubly robust estimator mayyield severely biased...

In this thesis, we address issues of model estimation for longitudinal categorical data and of model selection for these data with missing covariates. Longitudinal survey data capture the responses of each subject repeatedly through time, allowing for the separation of variation in the measured...

When sampling from a finite population to estimate the means or totals of K population characteristics of interest, survey designs typically impose the constraint that information on all K characteristics (or data items) is collected from all units in the sample. Relaxing this constraint means...