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Entropy 2016, 18(3), 97; doi:10.3390/e18030097

Constrained Inference When the Sampled and Target Populations Differ

Department of Mathematics, Troy University, Troy, AL 36082, USA
Department of Mathematics, Southern Illinois University, Carbondale, IL 62901, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 9 November 2015 / Revised: 23 February 2016 / Accepted: 7 March 2016 / Published: 16 March 2016
(This article belongs to the Section Information Theory)
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In the analysis of contingency tables, often one faces two difficult criteria: sampled and target populations are not identical and prior information translates to the presence of general linear inequality restrictions. Under these situations, we present new models of estimating cell probabilities related to four well-known methods of estimation. We prove that each model yields maximum likelihood estimators under those restrictions. The performance ranking of these methods under equality restrictions is known. We compare these methods under inequality restrictions in a simulation study. It reveals that these methods may rank differently under inequality restriction than with equality. These four methods are also compared while US census data are analyzed. View Full-Text
Keywords: categorical data; inequalities; least square; maximum likelihood; minimum chi-squared; raking categorical data; inequalities; least square; maximum likelihood; minimum chi-squared; raking

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yi, H.; Bhattacharya, B. Constrained Inference When the Sampled and Target Populations Differ. Entropy 2016, 18, 97.

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