Identifiability of zero-inflated Poisson models

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Zero-inflated Poisson (ZIP) models, which are mixture models, have been popularly used for count data that often contain large numbers of zeros, but their identifiability has not yet been thoroughly explored. In this work, we systematically investigate the identifiability of the ZIP models under a number of different assumptions. More specifically, we show the identifiability of a parametric ZIP model in which the incidence probability p(x) and Poisson mean λ(x) are modeled parametrically for x being a continuous covariate in a closed interval. A semiparametric ZIP regression model is shown to be identifiable in which for r(x) and s(x) being unspecified smooth functions.

Original languageEnglish (US)
Pages (from-to)306-312
Number of pages7
JournalBrazilian Journal of Probability and Statistics
Volume26
Issue number3
DOIs
StatePublished - Aug 2012

Fingerprint

Poisson Model
Identifiability
Zero
Poisson Regression
Closed interval
Count Data
Mixture Model
Smooth function
Covariates
Incidence
Regression Model
Siméon Denis Poisson

Keywords

  • Count data
  • Semiparametric zero-inflated Poisson (ZIP) regression model

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Identifiability of zero-inflated Poisson models. / Li, Chin-Shang.

In: Brazilian Journal of Probability and Statistics, Vol. 26, No. 3, 08.2012, p. 306-312.

Research output: Contribution to journalArticle

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