Robust empirical Bayes small area estimation with density power divergence

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

A two-stage normal hierarchical model called the Fay-Herriot model and the empirical Bayes estimator are widely used to obtain indirect and model-based estimates of means in small areas. However, the performance of the empirical Bayes estimator can be poor when the assumed normal distribution is misspecified. This article presents a simple modification that makes use of density power divergence and proposes a new robust empirical Bayes small area estimator. The mean squared error and estimated mean squared error of the proposed estimator are derived based on the asymptotic properties of the robust estimator of the model parameters. We investigate the numerical performance of the proposed method through simulations and an application to survey data.

Original languageEnglish
Pages (from-to)467-480
Number of pages14
JournalBiometrika
Volume107
Issue number2
DOIs
Publication statusPublished - 2020 Jun 1
Externally publishedYes

Keywords

  • Density power divergence
  • Empirical Bayes estimation
  • Fay-Herriot model

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Robust empirical Bayes small area estimation with density power divergence'. Together they form a unique fingerprint.

Cite this