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Empirical Best Prediction Under Unit-Level Logit Mixed Models

Author

Listed:
  • Hobza Tomáš

    (Department of Mathematics, Czech Technical University in Prague, Trojanova 13, 12000 Prague 2, Czech Republic.)

  • Morales Domingo

    (Operations Research Center, Miguel Hernández University of Elche, Avda. de la Universidad s/n, 03202 Elche, Spain.)

Abstract

The article applies unit-level logit mixed models to estimating small-area weighted sums of probabilities. The model parameters are estimated by the method of simulated moments (MSM). The empirical best predictor (EBP) of weighted sums of probabilities is calculated and compared with plug-in estimators. An approximation to the mean-squared error (MSE) of the EBP is derived and a bias-corrected MSE estimator is given and compared with parametric bootstrap alternatives. Some simulation experiments are carried out to study the empirical behavior of the model parameter MSM estimators, the EBP and plug-in estimators and the MSE estimators. An application to the estimation of poverty proportions in the counties of the region of Valencia, Spain, is given.

Suggested Citation

  • Hobza Tomáš & Morales Domingo, 2016. "Empirical Best Prediction Under Unit-Level Logit Mixed Models," Journal of Official Statistics, Sciendo, vol. 32(3), pages 661-692, September.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:3:p:661-692:n:6
    DOI: 10.1515/jos-2016-0034
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    References listed on IDEAS

    as
    1. Jiming Jiang & P. Lahiri, 2001. "Empirical Best Prediction for Small Area Inference with Binary Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 217-243, June.
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