Bayesian estimators in uncertain nested error regression models

Shonosuke Sugasawa, Tatsuya Kubokawa

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Nested error regression models are useful tools for the analysis of grouped data, especially in the context of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in each area is expressed as a mixture of a normal distribution and a positive mass at 0. For the estimation of the model parameters and prediction of the random effects, an objective Bayesian inference is proposed by setting non-informative prior distributions on the model parameters. Under mild sufficient conditions, it is shown that the posterior distribution is proper and the posterior variances are finite, confirming the validity of posterior inference. To generate samples from the posterior distribution, a Gibbs sampling method is provided with familiar forms for all the full conditional distributions. This paper also addresses the problem of predicting finite population means, and a sampling-based method is suggested to tackle this issue. Finally, the proposed model is compared with the conventional nested error regression model through simulation and empirical studies.

Original languageEnglish
Pages (from-to)52-63
Number of pages12
JournalJournal of Multivariate Analysis
Volume153
DOIs
Publication statusPublished - 2017 Jan 1
Externally publishedYes

Keywords

  • Bayesian estimator
  • Nested error regression model
  • Posterior propriety
  • Small area estimation
  • Uncertain random effect

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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