Fractional binomial regression model for count data with excess zeros

Music City Center 

In this talk, we introduce a new generalized linear model with fractional binomial distribution. Zero-inflated Poisson/negative binomial distributions are used for count data that has many zeros. To analyze the association of such a count variable with covariates, zero-inflated Poisson/negative binomial regression models are widely used. In this work, we develop a regression model with the fractional binomial distribution that can serve as an additional tool for modeling count data with excess zeros. The consistency of the ML estimators is proved under certain conditions, and the performance of the estimators is investigated with simulation results. Applications are provided with datasets from horticulture and public health, and the results show that on some occasions, our model outperforms the existing zero-inflated regression models.

Zero-inflated regression models

Count data with excess zeros

Fractional binomial distribution 

Korean International Statistical Society