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• W2245016916 abstract "We propose an empirical Bayes estimation method with spatially non-stationary hyperparameters for area-level discrete and continuous data. Observations are assumed to follow a natural exponential family distribution, and we use their conjugate priors as parameters. This enables us to obtain closed expressions for the Bayes estimators and the marginal likelihood function. We estimate the hyperparameters based on a geographically weighted regression with asymptotic properties. Then, using the hyperparameter estimators, we propose a geographically weighted empirical Bayes (GWEB) estimator of a quantity of interest. We also address a benchmarking problem arising from real applications by providing a benchmarked geographically weighted empirical Bayes estimator (BGWEB). In order to evaluate the risk of the GWEB and BGWEB estimators, we consider their mean squared errors (MSE) and their second-order unbiased estimators. Lastly, we investigate the performance of our methods via a simulation and apply them to real mortality rate data for Tokyo in 1930." @default.
• W2245016916 created "2016-06-24" @default.
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• W2245016916 date "2015-08-07" @default.
• W2245016916 modified "2023-09-27" @default.
• W2245016916 title "Geographically Weighted Empirical Bayes Estimation via Natural Exponential Family" @default.
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• W2245016916 hasPublicationYear "2015" @default.
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