Hierarchical and empirical Bayes small domain estimation of the proportion of persons without health insurance for minority subpopulations

Malay Ghosh, Dalho Kim, Karabi Sinha, Tapabrata Maiti, Myron Katzoff, Van L. Parsons

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

The paper considers small domain estimation of the proportion of persons without health insurance for different minority groups. The small domains are cross-classified by age, sex and other demographic characteristics. Both hierarchical and empirical Bayes estimation methods are used. Also, second order accurate approximations of the mean squared errors of the empirical Bayes estimators and bias-corrected estimators of these mean squared errors are provided. The general methodology is illustrated with estimates of the proportion of uninsured persons for several cross-sections of the Asian subpopulation.

Original languageEnglish (US)
Pages (from-to)53-66
Number of pages14
JournalSurvey Methodology
Volume35
Issue number1
StatePublished - Jun 22 2009
Externally publishedYes

Keywords

  • Asian
  • Bias-corrected
  • Mean squared error
  • Second order accurate

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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