Regression Models for Count Data in R

Zeileis, Achim and Kleiber, Christian and Jackman, Simon (2007) Regression Models for Count Data in R. Research Report Series / Department of Statistics and Mathematics, 53. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

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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. (author's abstract)

Item Type: Paper
Keywords: GLM / Poisson model / negative binomial model / zero-inflated model / hurdle model
Classification Codes: RVK QH 234
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 27 Apr 2007 11:29
Last Modified: 22 Oct 2019 00:41
URI: https://epub.wu.ac.at/id/eprint/1168

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