Constrained Inference When the Sampled and Target Populations Differ
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 r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/18/3/97 |
| Summary: | 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. |
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| ISSN: | 1099-4300 |