edoc

Regression models for count data in R

Zeileis, Achim and Kleiber, Christian and Jackman, Simon. (2008) Regression models for count data in R. Journal of Statistical Software, Vol. 27, H. 8. S. 1-25.

[img] PDF
Restricted to Repository staff only

716Kb

Official URL: http://edoc.unibas.ch/dok/A5252903

Downloads: Statistics Overview

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 hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() 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 hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros -- two problems that typically occur in count data sets in economics and the social 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)
UniBasel Contributors:Kleiber, Christian
Item Type:Article, refereed
Bibsysno:Link to catalogue
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Related URLs:
Last Modified:31 Dec 2015 10:47
Deposited On:22 Mar 2012 14:14

Repository Staff Only: item control page