المؤلفون: Tang, Xueying, Ghosh, Malay, Ha, Neung Soo, Sedransk, Joseph

المصدر: Journal of the American Statistical Association; Dec2018, Vol. 113 Issue 524, p1476-1489, 14p

مصطلحات موضوعية: SMALL area statistics, RANDOM effects model, POVERTY rate, BAYESIAN analysis, ESTIMATION theory, DATA analysis, SIMULATION methods & models

مستخلص: 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 (GL) 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 GL priors can improve estimation results in most cases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

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المؤلفون: Fay, Robert E., Herriot, Roger A.

المصدر: Journal of the American Statistical Association, 1979 Jun 01. 74(366), 269-277.

URL الوصول: https://www.jstor.org/stable/2286322