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Empirical best linear unbiased predictors in multivariate nested-error regression models

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  • Tsubasa Ito
  • Tatsuya Kubokawa

Abstract

For analyzing unit-level multivariate data in small area estimation, we consider the multivariate nested error regression model (MNER) and provide the empirical best linear unbiased predictor (EBLUP) of a small area characteristic based on second-order unbiased and consistent estimators of the ‘within’ and ‘between’ multivariate components of variance. The second-order approximation of the mean squared error (MSE) matrix of the EBLUP and its unbiased estimator are derived in closed forms. The confidence interval with second-order accuracy is also provided analytically.

Suggested Citation

  • Tsubasa Ito & Tatsuya Kubokawa, 2021. "Empirical best linear unbiased predictors in multivariate nested-error regression models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(10), pages 2224-2249, May.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:10:p:2224-2249
    DOI: 10.1080/03610926.2019.1662048
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    Cited by:

    1. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
    2. María Dolores Esteban & María José Lombardía & Esther López‐Vizcaíno & Domingo Morales & Agustín Pérez, 2022. "Empirical best prediction of small area bivariate parameters," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1699-1727, December.

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