Resampling Based Empirical Prediction: An Application to Small Area Estimation

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

Best linear unbiased prediction is well known for its wide range of applications including small area estimation. While the theory is well established for mixed linear models and under normality of the error and mixing distributions, the literature is sparse for nonlinear mixed models under nonnormality of the error or of the mixing distributions. This article develops a resampling based unified approach for predicting mixed effects under a generalized mixed model set up. Second order accurate nonnegative estimators of mean squared prediction errors are also developed. Given the parametric model, the proposed methodology automatically produces estimates of the small area parameters and their MSPEs, without requiring explicit analytical expressions for the MSPE.
31 pages

Keywords

Statistics Theory, 62E20

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/131033

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0604513
 http://arxiv.org/abs/math/0604513

Collections

arXiv