von Rosen, Dietrich
- Department of Energy and Technology, Swedish University of Agricultural Sciences
- Linköping University
Research article2019Peer reviewedOpen access
Ngaruye, Innocent; Von Rosen, Dietrich; Singull, Martin
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.
Mean-squared errors; Multivariate linear model; Parametric bootstrap; Repeated measures data; Small area estimation
Communications in Statistics - Theory and Methods
2019, Volume: 48, number: 8, pages: 2060-2073 Publisher: TAYLOR & FRANCIS INC
Probability Theory and Statistics
DOI: https://doi.org/10.1080/03610926.2018.1444178
https://res.slu.se/id/publ/101015