Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/13523
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dc.contributor.authorNandram, Balgobin
dc.contributor.authorBhatta, Dilli
dc.contributor.authorBhadra, Dhiman
dc.date.accessioned2015-05-12T10:43:05Z
dc.date.available2015-05-12T10:43:05Z
dc.date.issued2015
dc.identifier.citationJournal of Statistical Computation and Simulation Volume 85(2), 2015
dc.identifier.issn00949655
dc.identifier.urihttp://hdl.handle.net/11718/13523
dc.description.abstractWe consider a likelihood ratio test of independence for large two-way contingency tables having both structural (non-random) and sampling (random) zeros in many cells. The solution of this problem is not available using standard likelihood ratio tests. One way to bypass this problem is to remove the structural zeroes from the table and implement a test on the remaining cells which incorporate the randomness in the sampling zeros; the resulting test is a test of quasi-independence of the two categorical variables. This test is based only on the positive counts in the contingency table and is valid when there is at least one sampling (random) zero. The proposed (likelihood ratio) test is an alternative to the commonly used ad hoc procedures of converting the zero cells to positive ones by adding a small constant. One practical advantage of our procedure is that there is no need to know if a zero cell is structural zero or a sampling zero. We model the positive counts using a truncated multinomial distribution. In fact, we have two truncated multinomial distributions; one for the null hypothesis of independence and the other for the unrestricted parameter space. We use Monte Carlo methods to obtain the maximum likelihood estimators of the parameters and also thep-value of our proposed test. To obtain the sampling distribution of the likelihood ratio test statistic, we use bootstrap methods. We discuss many examples, and also empirically compare the power function of the likelihood ratio test relative to those of some well-known test statistics.en_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectChi-squared testen_US
dc.subjectMonte Carlo methodsen_US
dc.subjectQuasi-independenceen_US
dc.subjectZero countsen_US
dc.titleA likelihood ratio test of quasi-independence for sparse two-way contingency tablesen_US
dc.typeArticleen_US
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