TY - JOUR
T1 - Small area estimation with spatially varying natural exponential families
AU - Sugasawa, Shonosuke
AU - Kawakubo, Yuki
AU - Ogasawara, Kota
N1 - Funding Information:
This work was supported by Japan Society for the Promotion of Science (KAKENHI) Grant Numbers JP18K12757, JP19K13667 and JP16K17153. We are thankful to Tatsuya Kubokawa and Hisashi Noma for their valuable comments.
Publisher Copyright:
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/4/12
Y1 - 2020/4/12
N2 - 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.
AB - 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.
KW - Empirical Bayes estimation
KW - geographically weighted regression
KW - mean squared error
KW - natural exponential family with quadratic variance function
KW - small area estimation
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U2 - 10.1080/00949655.2020.1714048
DO - 10.1080/00949655.2020.1714048
M3 - Article
AN - SCOPUS:85078475106
SN - 0094-9655
VL - 90
SP - 1039
EP - 1056
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 6
ER -