<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Empirical Best Prediction Under Unit-Level Logit Mixed Models

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Empirical Best Prediction Under Unit-Level Logit Mixed Models"}

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...

Empirical Best Prediction Under Unit-Level Logit Mixed Models

Online veröffentlicht: 23. Sept. 2016

Seitenbereich: 661 - 692

Eingereicht: 01. Feb. 2015

Akzeptiert: 01. Mai 2016

DOI: https://doi.org/10.1515/jos-2016-0034

Schlüsselwörter
Poverty, method of moments, logit mixed models, empirical best predictor, mean-squared error, bootstrap

© 2016 Tomáš Hobza et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Empirical Best Prediction Under Unit-Level Logit Mixed Models

Online veröffentlicht: 23. Sept. 2016

Seitenbereich: 661 - 692

Eingereicht: 01. Feb. 2015

Akzeptiert: 01. Mai 2016

DOI: https://doi.org/10.1515/jos-2016-0034

SchlüsselwörterPoverty, method of moments, logit mixed models, empirical best predictor, mean-squared error, bootstrap

© 2016 Tomáš Hobza et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Poverty, method of moments, logit mixed models, empirical best predictor, mean-squared error, bootstrap