Resampling-based empirical prediction: an application to small area estimation |
| |
Authors: | Lahiri Soumendra N; Maiti Tapabrata; Katzoff Myron; Parsons Van |
| |
Institution: | Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A. |
| |
Abstract: | Best linear unbiased prediction is well known for its wide rangeof applications including small area estimation. While the theoryis well established for mixed linear models and under normalityof the error and mixing distributions, the literature is sparsefor nonlinear mixed models under nonnormality of the error distributionor of the mixing distributions. We develop a resampling-basedunified approach for predicting mixed effects under a generalizedmixed model set-up. Second-order-accurate nonnegative estimatorsof mean squared prediction errors are also developed. Giventhe parametric model, the proposed methodology automaticallyproduces estimators of the small area parameters and their meansquared prediction errors, without requiring explicit analyticalexpressions for the mean squared prediction errors. |
| |
Keywords: | Best predictor Bootstrap Kernel Mean squared prediction error |
本文献已被 Oxford 等数据库收录! |
|