Marginalized zero-inflated generalized Poisson regression

Felix Famoye, John S. Preisser

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The generalized Poisson (GP) regression model has been used to model count data that exhibit over-dispersion or under-dispersion. The zero-inflated GP (ZIGP) regression model can additionally handle count data characterized by many zeros. However, the parameters of ZIGP model cannot easily be used for inference on overall exposure effects. In order to address this problem, a marginalized ZIGP is proposed to directly model the population marginal mean count. The parameters of the marginalized zero-inflated GP model are estimated by the method of maximum likelihood. The regression model is illustrated by three real-life data sets.

Original languageEnglish
Pages (from-to)1247-1259
Number of pages13
JournalJournal of Applied Statistics
Volume45
Issue number7
DOIs
StatePublished - May 19 2018

Keywords

  • Count data modeling
  • dispersion
  • health-care utilization
  • mixture
  • zero-inflation

Fingerprint

Dive into the research topics of 'Marginalized zero-inflated generalized Poisson regression'. Together they form a unique fingerprint.

Cite this