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James Sweeney
Andrew Parnell
John Haslett



mixture models treatment general family inflation count data modelling bayesian framework

A general framework for modelling zero inflation (2018)

Abstract We propose a new framework for the modelling of count data exhibiting zero inflation (ZI). The main part of this framework includes a new and more general parameterisation for ZI models which naturally includes both over- and under-inflation. It further sheds new theoretical light on modelling and inference and permits a simpler alternative, which we term as multiplicative, in contrast to the dominant mixture and hurdle models. Our approach gives the statistician access to new types of ZI of which mixture and hurdle are special cases. We outline a simple parameterised modelling approach which can help to infer both ZI type and degree and provide an underlying treatment that shows that current ZI models are themselves typically within the exponential family, thus permitting much simpler theory, computation and classical inference. We outline some possibilities for a natural Bayesian framework for inference; and a rich basis for work on correlated ZI counts. The present paper is an incomplete report on the underlying theory. A later version will include computational issues and provide further examples.
Collections Ireland -> Maynooth University -> Academic Unit = Faculty of Science and Engineering: Research Institutes: Hamilton Institute
Ireland -> Maynooth University -> Status = Published
Ireland -> Maynooth University -> Type = Monograph

Full list of authors on original publication

James Sweeney, Andrew Parnell, John Haslett

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Andrew Parnell
Maynooth University
Total Publications: 45