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Transition models for count data: a flexible alternative to fixed distribution models

Author

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  • Moritz Berger

    (Medizinische Fakultät, Universität Bonn)

  • Gerhard Tutz

    (Ludwig-Maximilians-Universität München)

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.

Suggested Citation

  • Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    • Handle: RePEc:spr:stmapp:v:30:y:2021:i:4:d:10.1007_s10260-021-00558-6
    DOI: 10.1007/s10260-021-00558-6
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    References listed on IDEAS

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