Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286850 
Year of Publication: 
2021
Citation: 
[Journal:] Statistical Methods & Applications [ISSN:] 1613-981X [Volume:] 30 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 1259-1283
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
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.
Subjects: 
Count data
Smoothing
Transition model
Varying coefficients
Zero-inflated model
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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