On Measuring the Variability of Small Area Estimators in a Multivariate Fay-Herriot Model
Statistics Theory
2018-04-27 v1 Statistics Theory
Abstract
This paper is concerned with the small area estimation in the multivariate Fay-Herriot model where covariance matrix of random effects are fully unknown. The covariance matrix is estimated by a Prasad-Rao type consistent estimator, and the empirical best linear un- biased predictor (EBLUP) of a vector of small area characteristics is provided. When the EBLUP is measured in terms of a mean squared error matrix (MSEM), a second-order approximation of MSEM of the EBLUP and a second-order unbiased estimator of the MSEM is derived analytically in closed forms. The performance is investigated through numerical and empirical studies.
Keywords
Cite
@article{arxiv.1804.09941,
title = {On Measuring the Variability of Small Area Estimators in a Multivariate Fay-Herriot Model},
author = {Tsubasa Ito and Tatsuya Kubokawa},
journal= {arXiv preprint arXiv:1804.09941},
year = {2018}
}