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Estimation of mean squared error of model-based small area estimators

Author

Listed:
  • Gauri Sankar Datta

    (Department of Statistics, University of Georgia)

  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

  • J. N. K. Rao

    (School of Mathematics and Statistics, Carleton University)

  • Isabel Molina

    (Department of Statistics, University Carlos III de Madrid)

Abstract

Estimation of small area means under a basic area level model is studied, using an empirical Bayes (best) estimator or a weighted estimator with fixed weights. Mean squared errors (MSEs) of the estimators and nearly unbiased (or exactly unbiased) estimators of MSE are derived under three different approaches: design based (approach 1), unconditional model based (approach 2) and conditional model based (approach 3). Performance of MSE estimators under the three approaches with respect to relative bias and coefficient of variation is also studied, using a simulation experiment.

Suggested Citation

  • Gauri Sankar Datta & Tatsuya Kubokawa & J. N. K. Rao & Isabel Molina, 2009. "Estimation of mean squared error of model-based small area estimators," CIRJE F-Series CIRJE-F-633, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2009cf633
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