Global-Local Priors for Small Area Estimation
Friday, September 22, 2017 - 3:30pm
Small area estimation is becoming increasingly popular for survey statisticians. One very important program is Small Area Income and Poverty Estimation undertaken by the United States Bureau of the Census, which aims at providing estimates related to income and poverty based on American Community Survey data at the state level and even at lower levels of geography. This article introduces global-local shrinkage priors for random effects in small area estimation to capture wide area level variation when the number of small areas is very large. These priors employ two levels of parameters, global and local parameters, to express variances of area-specific random effects so that both small and large random effects can be captured properly. We show via simulations and data analysis that use of the global-local priors can improve estimation results in most cases.
Keywords: Bayesian model, Fay-Herriot model, poverty rate, spike-and-slab prior
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