Abstract

In this paper, we discuss the derivation of the first and second moments for the proposed small area estimators under a multivariate linear model for repeated measures data. The aim is to use these moments to estimate the mean-squared errors (MSE) for the predicted small area means as a measure of precision. At the first stage, we derive the MSE when the covariance matrices are known. At the second stage, a method based on parametric bootstrap is proposed for bias correction and for prediction error that reflects the uncertainty when the unknown covariance is replaced by its suitable estimator.

Keywords

Mean-squared errors; Multivariate linear model; Parametric bootstrap; Repeated measures data; Small area estimation

Published in

Communications in Statistics - Theory and Methods
2019, Volume: 48, number: 8, pages: 2060-2073
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/10.1080/03610926.2018.1444178

Permanent link to this page (URI)

https://res.slu.se/id/publ/101015