Small Area Predictors with Dual Shrinkage of Means and Variances

2015/07/29 by Hiromasa Tamae, Tamae, Hiromasa, Tatsuya Kubokawa +1
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.1507.08069

openalex publication_date 2015/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The paper concerns small-area estimation in the Fay-Herriot type area-level model with random dispersions, which models the case that the sampling errors change from area to area. The resulting Bayes estimator shrinks both means and variances, but needs numerical computation to provide the estimates. In this paper, an approximated empirical Bayes (AEB) estimator with a closed form is suggested. The model parameters are estimated via the moment method, and the mean squared error of the AEB is estimated via the single parametric bootstrap method. The benchmarked estimator and a second-order unbiased estimator of the mean squared error are also derived.

Citations

• Small Area Estimation‐New Developments and Directions

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