Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

Williams Matthew () and Berg Emily ()
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Williams Matthew: Research and Development Division, National Agricultural Statistics Service, U. S. Department of Agriculture,Fairfax, VA 22030, U.S.A.
Berg Emily: Department of Statistics, Iowa State University, Ames, IA 50011, U.S.A.

Journal of Official Statistics, 2013, vol. 29, issue 3, 375-396

Abstract: We examine the incorporation of analyst input into the constrained estimation process. In the calibration literature, there are numerous examples of estimators with “optimal” properties. We show that many of these can be derived from first principles. Furthermore, we provide mechanisms for injecting user input to create user-constrained optimal estimates. We include derivations for common deviance measures with linear and nonlinear constraints and we demonstrate these methods on a contingency table and a simulated survey data set. R code and examples are available at https://github.com/mwilli/Constrained-estimation.git.

Keywords: Calibration; general deviance measures; nonlinear constraints; raking; user feedback (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:29:y:2013:i:3:p:375-396:n:7

DOI: 10.2478/jos-2013-0032

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