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Empirical bayes estimation of demographic schedules for small areas
Authors:Renato M. Assunção  Carl P. Schmertmann  Joseph E. Potter  Suzana M. Cavenaghi
Affiliation:(1) United States Department of Agriculture, Agricultural Research Service (USDA-ARS), Department of Agronomy, Iowa State University, Ames, IA 50014, USA;(2) Section on Statistical Genetics, Department of Biostatistics, RPHB 327, 1530 3rd Ave. S., Birmingham, AL 35294-0022, USA;(3) Department of Mathematics and Statistics, University of Missouri-Rolla, 202 Rolla Building, Rolla, MO, 65409, USA;(4) Department of Psychiatry/Westchester, Cornell Institute of Geriatric Psychiatry, Weill Medical College of Cornell University, 21 Bloomingdale Road, White Plains, NY 10605025, USA;(5) Department of Medicine and the Wisconsin Primate Research Center, University of Wisconsin, Madison, WI, USA;(6) The Geriatric Research, Education, and Clinical Center, William S. Middleton VA Hospital, Madison, WI 53705, USA;(7) Clinical Nutrition Research Center, WEBB Building, Room 402, 1530 3rd Ave. S., Birmingham, AL 35294-3360, USA;(8) Life Gen Technologies, LLC c/o Mirus Corporation, 505 S. Rosa Rd., Madison, WI 53719, USA;
Abstract:In this article, we analyze empirical Bayes (EB) methods for estimating small-area rate schedules. We develop EB methods that treat schedules as vectors and use adaptive neighborhoods to keep estimates appropriately local. This method estimates demographic rates for local subpopulations by borrowing strength not only from similar individuals elsewhere but also from other groups in the same area and from regularities in schedules across locations. EB is substantially better than standard methods when rates have strong spatial and age patterns. We illustrate this method with estimates of age-specific fertility schedules for over 3,800 Brazilian municipalities.
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