Confidence intervals are one of the most useful statistical tools. This dissertation is a study of several methods for forming confidence intervals that are insensitive to model assumptions, provided that the mean model for the data is not misspecified. The most commonly used robust confidence...

We propose a method of simultaneous model selection and estimation in additive regression models (ARMs) forindependent normal data. We use the mixed model representation of the smoothing spline estimators of thenonparametric functions in ARMs, where the importance of these functions is...

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...

Model selection is important for longitudinal data analysis. But up to date little work has been done on variable selection for generalized linear mixed models (GLMM). In this paper we propose and study a class of variable selection methods. Full likelihood (FL) approach is proposed for...

This study examines the effect of increasing environmentalregulation on hog production in the U.S. domestic market and on thecompetitiveness of U.S. pork in the international market. Agriculturalpollution from animal feeding operations (AFOs) is becoming a majorenvironmental consideration in the...

Measuring risk is a crucial aspect of the portfolio optimization problem in finance, and of capital adequacy assessment in risk management. Expected Shortfall (ES) has been proposed as a coherent risk measure, by contrast with Value-at-Risk (VaR) and the standard-deviation-type of measures....

We develop a framework for establishing sample sizes in breeding plans, so that one is able to estimate narrow-sense heritability with smallest possible variance, for a given amount of effort. We call this an optimal genetic design. The framework allows one to compare the variances of estimators...

We present two deconvolution estimators for the density function of a random variable X that is measured with error. The first estimates the density of X from the set of independent replicate measurements W[subscript r,j], where W[subscript r,j]=X[subscript x]+U[subscript r,j] for r=1,...,n and...

The application of the bootstrap to spatially correlated data has not been studied as widely as its application to time series data. This is a challenging problem since it is difficult to preserve the correlation structure of the data while implementing the bootstrap method. Kunsch (1989),...

Censored median regression models have been shown to be useful for analyzing a variety of censored survival data with the robustness property. We study sparse estimation and inference of censored median regression. The new method minimizes an inverse censoring probability weighted least absolute...