Small area estimation under a multivariate linear model for repeated measures data

Abstract

In this article, small area estimation under a multivariate linear model for repeated measures data is considered. The proposed model aims to get a model which borrows strength both across small areas and over time. The model accounts for repeated surveys, grouped response units, and random effects variations. Estimation of model parameters is discussed within a likelihood based approach. Prediction of random effects, small area means across time points, and per group units are derived. A parametric bootstrap method is proposed for estimating the mean squared error of the predicted small area means. Results are supported by a simulation study.

Keywords

Maximum likelihood; multivariate linear model; prediction of random effects; repeated measures data; small area estimation; 62F10; 62H12; 62D05

Published in

Communications in Statistics - Theory and Methods
2017, volume: 46, number: 21, pages: 10835-10850
Publisher: TAYLOR & FRANCIS INC

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    
  • Linköping University

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1080/03610926.2016.1248786

Permanent link to this page (URI)

https://res.slu.se/id/publ/92641