<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

Research and Development Division, National Agricultural Statistics Service, U. S. Department of Agriculture,Fairfax, VA 22030, U.S.A.

Department of Statistics, Iowa State University, Ames, IA 50011, U.S.A.

Journal of Official Statistics

We examine the incorporation of analyst input into the constrained estimation process. In the calibration literature, there are numerous examples of estimators...

Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

Published Online: Oct 03, 2013

Page range: 375 - 396

DOI: https://doi.org/10.2478/jos-2013-0032

Keywords
Calibration, general deviance measures, nonlinear constraints, raking, user feedback

This content is open access.

Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

Published Online: Oct 03, 2013

Page range: 375 - 396

DOI: https://doi.org/10.2478/jos-2013-0032

KeywordsCalibration, general deviance measures, nonlinear constraints, raking, user feedback

This content is open access.

Keywords
Calibration, general deviance measures, nonlinear constraints, raking, user feedback