Techniques for High Accuracy in Estimation of the Mean Squared Prediction Error in General Small-Area Model

• Activity Number: 554
• Type: Invited
• Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Government Statistics
• Author(s): Ansu Chatterjee*+ and Partha Lahiri+
• Companies: University of Minnesota and University of Maryland
• Keywords: Small domain ; resampling methods ; sample survey ; mixed models ; risk function ; EBP

Abstract

A general small area model is a hierarchical two-stage model, of which special cases are mixed linear models, generalized linear mixed models and hierarchical generalized linear models. In such models, the variability of predictors (like the empirical best predictor or the empirical best linear unbiased predictor)can be quantified with their mean squared prediction error (MSPE), or other risk functions. Estimators for the MSPE are generally not available outside some special cases. First, we propose a simple resampling-based estimation of MSPE for any general small area model. Second, we propose three techniques for improving on the basic, resampling-based MSPE estimator to achieve high order accuracy. Computational issues and other properties of these improved MSPE estimators will be discussed.