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Modelling Correlated zero-inflated count data

Modelling correlated zero-inflated count data
Authors: Dobbie, Melissa J.; Welsh, A.H.;

Modelling Correlated zero-inflated count data

Abstract

Summary: This paper extends the two-component approach to modelling count data with extra zeros, considered by Mullahy (1986), Heilbron (1994) and Welsh et al. (1996), to take account of possible serial dependence between repeated observations. Generalized estimating equations (Liang \& Zeger, 1986) are constructed for each component of the model by incorporating correlation matrices into each of the maximum likelihood estimating equations. The proposed method is demonstrated on weekly counts of Noisy Friarbirds (Philemon cornic-ulatus), which were recorded by observers for the Canberra Garden Bird Survey (Hermes, 1981).

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United Kingdom, Australia
Related Organizations
Keywords

Linear inference, regression, Generalized estimating equation (GEE), Zero-inflated counts, Correlated data, Truncated Poisson distribution, Marginal model, 510, Applications of statistics to biology and medical sciences; meta analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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