Hurdle Model for Zero-Inflated Count Data from Mobile Phone Market

For count responses, the situation of excess zeros(disproportionately more zeros than what standard models allow) often occurs in management and sociological applications. The zero-inflated Poisson regression model has been used for count data with excess zeros. An alternative approach is the hurdle model, in which the second part is governed by a truncated Poisson or negative binomial distribution. We apply the models to draw the relation between the frequency of moving mobile phone companies and customers social attributes such as age, income and so on. We compare the models to the Poisson model in terms of log-likelihood.