Small area estimation with spatially varying natural exponential families

Shonosuke Sugasawa, Yuki Kawakubo, Kota Ogasawara

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Two-stage hierarchical models have been widely used in small area estimation to produce indirect estimates of areal means. When the areas are treated exchangeably and the model parameters are assumed to be the same over all areas, we might lose the efficiency in the presence of spatial heterogeneity. To overcome this problem, we consider a two-stage area-level model based on natural exponential family with spatially varying model parameters. We employ geographically weighted regression approach to estimating the varying parameters and suggest a new empirical Bayes estimator of the areal mean. We also discuss some related problems, including the mean squared error estimation, benchmarked estimation and estimation in non-sampled areas. The performance of the proposed method is evaluated through simulations and applications to two data sets.

Original languageEnglish
Pages (from-to)1039-1056
Number of pages18
JournalJournal of Statistical Computation and Simulation
Volume90
Issue number6
DOIs
Publication statusPublished - 2020 Apr 12
Externally publishedYes

Keywords

  • Empirical Bayes estimation
  • geographically weighted regression
  • mean squared error
  • natural exponential family with quadratic variance function
  • small area estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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