GEE-based zero-inflated generalized Poisson model for clustered over or under-dispersed count data

The zero-inflated regression models such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB) or zero-inflated generalized Poisson (ZIGP) regression models can model the count data with excess zeros. The ZINB model can handle over-dispersed and the ZIGP model can handle the over or...

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Veröffentlicht in:Journal of statistical computation and simulation Jg. 89; H. 14; S. 2711 - 2732
Hauptverfasser: Sarvi, Fatemeh, Moghimbeigi, Abbas, Mahjub, Hossein
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 22.09.2019
Taylor & Francis Ltd
Schlagworte:
Data acquisition
Dispersion
Economic models
expectation-solution algorithm
generalized estimating equation
generalized Poisson regression
Poisson density functions
Regression analysis
Regression models
Statistical analysis
zero-inflation
ISSN:0094-9655, 1563-5163
Online-Zugang:Volltext
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Zusammenfassung:The zero-inflated regression models such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB) or zero-inflated generalized Poisson (ZIGP) regression models can model the count data with excess zeros. The ZINB model can handle over-dispersed and the ZIGP model can handle the over or under-dispersed count data with excess zeros as well. Moreover, the count data may be correlated because of data collection procedure or special study design. The clustered sampling approach is one of the examples in which the correlation among subjects could be defined. In such situations, a marginal model using generalized estimating equation (GEE) approach can incorporate these correlations and lead up to the relationships at the population level. In this study, the GEE-based zero-inflated generalized Poisson regression model was proposed to fit over and under-dispersed clustered count data with excess zeros.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2019.1632857