Small area estimation with spatially varying natural exponential families. Issue 6 (12th April 2020)

• Record Type: Journal Article
• Authors: Sugasawa, Shonosuke; Kawakubo, Yuki; Ogasawara, Kota
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 6(2020)
• Page Start: 1039
• Page End: 1056
• Publication Date: 2020-04-12
• Subjects: Empirical Bayes estimation -- geographically weighted regression -- mean squared error -- natural exponential family with quadratic variance function -- small area estimation; 62C12; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1714048
• Languages: English
• ISSNs: 0094-9655

Abstract

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.