Multivariate Fay-Herriot models for small area estimation

Abstract

Introduction Multivariate Fay–Herriot models for estimating small area indicators are introduced. Among the available procedures for fitting linear mixed models, the residual maximum likelihood (REML) is employed. The empirical best predictor (EBLUP) of the vector of area means is derived. An approximation to the matrix of mean squared crossed prediction errors (MSE) is given and four MSE estimators are proposed. The first MSE estimator is a plug-in version of the MSE approximation. The remaining MSE estimators combine parametric bootstrap with the analytic terms of the MSE approximation.

DOI: 10.1016/j.csda.2015.07.013

Cite this paper

@article{Benavent2016MultivariateFM, title={Multivariate Fay-Herriot models for small area estimation}, author={Roberto Benavent and Domingo Morales}, journal={Computational Statistics & Data Analysis}, year={2016}, volume={94}, pages={372-390} }