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Zero-Inflated Negative Binomial Regression

Zero-Inflated Negative Binomial regression is a count model, introduced by Greene (1994), that handles count data showing both an excess of zeros and overdispersion. It combines a binary inflation process that generates structural zeros with a negative binomial count process, making it one of the most widely used distributions for real-world count data.

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Zero-Inflated Negative Binomial (ZINB) Regression

Taksonomisk metoderegistrering · regression-model / statistics

Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper. · URL

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