Fitting Zero-Inflated Count Data Models by Using PROC GENMOD
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چکیده
Count data sometimes exhibit a greater proportion of zero counts than is consistent with the data having been generated by a simple Poisson or negative binomial process. For example, a preponderance of zero counts have been observed in data that record the number of automobile accidents per driver, the number of criminal acts per person, the number of derogatory credit reports per person, the number of incidences of a rare disease in a population, and the number of defects in a manufacturing process, just to name a few. Failure to properly account for the excess zeros constitutes a model misspecification that can result in biased or inconsistent estimators.
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