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Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

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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.)

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.

Suggested Citation

  • Williams Matthew & Berg Emily, 2013. "Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates," Journal of Official Statistics, Sciendo, vol. 29(3), pages 375-396, June.
    • Handle: RePEc:vrs:offsta:v:29:y:2013:i:3:p:375-396:n:7
    DOI: 10.2478/jos-2013-0032
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    References listed on IDEAS

    as
    1. Ted Chang & Phillip S. Kott, 2008. "Using calibration weighting to adjust for nonresponse under a plausible model," Biometrika, Biometrika Trust, vol. 95(3), pages 555-571.
    2. Adrian Pizzinga, 2010. "Constrained Kalman Filtering: Additional Results," International Statistical Review, International Statistical Institute, vol. 78(2), pages 189-208, August.
    3. J. Chen, 2002. "Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys," Biometrika, Biometrika Trust, vol. 89(1), pages 230-237, March.
    4. D'Arrigo, Julia & Skinner, Chris J., 2010. "Linearization variance estimation for generalized raking estimators in the presence of nonresponse," LSE Research Online Documents on Economics 39120, London School of Economics and Political Science, LSE Library.
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