A flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

引入了一种灵活的半参数模型，它为计数数据的经典回归模型（例如泊松模型和负二项式模型）提供了一种替代方案，也为考虑过零的更通用的模型（也基于固定的分布假设）提供了一种替代方法。该模型允许数据本身确定响应变量的分布，但是在其基本形式中，使用参数项来指定解释变量的作用。另外，考虑了扩展版本，其中协变量的影响是非参数指定的。在仿真中，通过利用卫生和社会科学领域的一些实际数据应用程序，对提议的模型和传统模型进行了比较。