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dc.contributor.authorCzado, Claudiaen_US
dc.contributor.authorMin, Alekseyen_US
dc.description.abstractCount data often exhibit overdispersion and/or require an adjustment for zero outcomes with respect to a Poisson model. Zero-modified Poisson (ZMP) and zeromodified generalized Poisson (ZMGP) regression models are useful classes of models for such data. In the literature so far only score tests are used for testing the necessity of this adjustment. For this testing problem we show how poor the performance of the corresponding score test can be in comparison to the performance of Wald and likelihood ratio (LR) tests through a simulation study. In particular, the score test in the ZMP case results in a power loss of 47% compared to the Wald test in the worst case, while in the ZMGP case the worst loss is 87%. Therefore, regardless of the computational advantage of score tests, the loss in power compared to the Wald and LR tests should not be neglected and these much more powerful alternatives should be used instead. We also prove consistency and asymptotic normality of the maximum likelihood estimators in the above mentioned regression models to give a theoretical justification for Wald and likelihood ratio tests.en_US
dc.publisher|aTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen|cMünchenen_US
dc.relation.ispartofseries|aDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München|x474en_US
dc.subject.keywordgeneralized Poisson distributionen_US
dc.subject.keywordlikelihood ratio testen_US
dc.subject.keywordmaximum likelihood Estimatoren_US
dc.subject.keywordscore testen_US
dc.subject.keywordWald testen_US
dc.titleTesting for zero-modification in count regression modelsen_US
dc.type|aWorking Paperen_US

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