Zero-Inflated Regression Models for Modeling the Effect of air Pollutants on Hospital Admissions

Cengiz M. A.

POLISH JOURNAL OF ENVIRONMENTAL STUDIES, vol.21, no.3, pp.565-568, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 3
  • Publication Date: 2012
  • Journal Name: POLISH JOURNAL OF ENVIRONMENTAL STUDIES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.565-568
  • Keywords: count regression, zero-inflated models, air pollution, OBSTRUCTIVE PULMONARY-DISEASE, POISSON REGRESSION, POLLUTION, ABUNDANCE
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

Count regression methods are the fundamental tool used for modeling the association between environmental pollution and hospital admissions. Data with many zeros are often encountered in count regression models. Failure to account for the extra zeros may result in biased parameter estimates and misleading inferences. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for situations where the data generating process results in too many zeros.