Empirical Best Linear Unbiased Predictors in Multivariate Nested-Error Regression Models
Statistics Theory
2018-04-27 v1 Statistics Theory
Abstract
For analyzing unit-level multivariate data in small area estimation, we consider the multivariate nested error regression model (MNER) and provide the empirical best linear unbiased predictor (EBLUP) of a small area characteristic based on second-order unbiased and consistent estimators of the `within' and `between' multivariate components of variance. The second-order approximation of the mean squared error (MSE) matrix of the EBLUP and its unbiased estimator are derived in closed forms. The confidence interval with second-order accuracy is also provided analytically.
Cite
@article{arxiv.1804.09940,
title = {Empirical Best Linear Unbiased Predictors in Multivariate Nested-Error Regression Models},
author = {Tsubasa Ito and Tatsuya Kubokawa},
journal= {arXiv preprint arXiv:1804.09940},
year = {2018}
}