Small area estimation under a multivariate linear model for repeated measures data |
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| Authors: | Innocent Ngaruye Joseph Nzabanita Dietrich von Rosen Martin Singull |
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| Affiliation: | 1. Department of Mathematics, Link?ping University Link?ping, Sweden;2. Department of Mathematics, College of Science and Technology, University of Rwanda, Kigali, Rwandainnocent.ngaruye@liu.se;4. Department of Mathematics, College of Science and Technology, University of Rwanda, Kigali, Rwanda;5. Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden |
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| 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. |
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| Keywords: | Maximum likelihood multivariate linear model prediction of random effects repeated measures data small area estimation. |
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