This collaborative research investigates various problems associated with parametric empirical Bayes point and interval estimation and measure of uncertainty of a parametric empirical Bayes small-area estimator when the data are obtained from a complex survey. In addition, the investigators will conduct three real world applications of parametric empirical Bayes analysis: (1) Estimation of U.S. Census undercount; (2) Estimation of the median income of four-person families for fifty U.S. States and the District of Columbia; and (3) Estimation of the unemployment rates for fifty U.S. states and the District of Columbia. There is a growing demand by many U.S. and overseas federal agencies to produce reliable small area statistics for various subgroups of a population. Usual design-based survey estimators are not suitable for this purpose since a typical sample survey being designed for a large population contains very little information regarding the sub-populations or small areas of interest. The problem is generally referred to as a small-area (domain) estimation problem in the sample survey literature. Development of reliable small-area statistics and suitable measures of uncertainty using information from complex surveys is extremely important. This research will advance small-area estimation methods.
