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Regression Models for Count Data in R

Jackman, Simon and Kleiber, Christian and Zeileis, Achim. (2007) Regression Models for Count Data in R. WWZ Discussion Papers, 2007 (24).

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Official URL: https://edoc.unibas.ch/61818/

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Abstract

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model classes are able to incorporate over-dispersion and excess zeros - two problems that typically occur in count data sets in economics and the social and political sciences - better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > ├ľkonometrie und Statistik (Kleiber)
12 Special Collections > WWZ Publications > WWZ Discussion Papers and Working Papers
UniBasel Contributors:Kleiber, Christian
Item Type:Working Paper
Publisher:WWZ, University of Basel
Note:Publication type according to Uni Basel Research Database: Discussion paper / Internet publication
Identification Number:
  • handle: RePEc:bsl:wpaper:2007/24
Last Modified:14 Mar 2018 09:29
Deposited On:13 Mar 2018 13:18

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