Generalized poisson models arising from Markov processes

Bosch, RJ; Ryan, LM

Generalized poisson models arising from Markov processes

Bosch, RJ Ryan, LM

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Publication Type:: Journal Article
Citation:: Statistics and Probability Letters, 1998, 39 (3), pp. 205 - 212
Issue Date:: 1998-08-15

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Field	Value	Language
dc.contributor.author	Bosch, RJ	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	1998-08-15	en_US
dc.identifier.citation	Statistics and Probability Letters, 1998, 39 (3), pp. 205 - 212	en_US
dc.identifier.issn	0167-7152	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26186
dc.description.abstract	We develop a family of distributions which allow for over-and underdispersion relative to the Poisson. This latter feature is particularly appealing since many existing methods only allow for overdispersion. These distributions arise from underlying continuous-time Markov processes in which event rates depend on how many events have already occurred. The results are illustrated with underdispersed count data from a polyspermy study and overdispersed data from the Canadian Sickness Survey. @ 1998 Elsevier Science B.V. All rights reserved.	en_US
dc.relation.ispartof	Statistics and Probability Letters	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Generalized poisson models arising from Markov processes	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	39	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

We develop a family of distributions which allow for over-and underdispersion relative to the Poisson. This latter feature is particularly appealing since many existing methods only allow for overdispersion. These distributions arise from underlying continuous-time Markov processes in which event rates depend on how many events have already occurred. The results are illustrated with underdispersed count data from a polyspermy study and overdispersed data from the Canadian Sickness Survey. @ 1998 Elsevier Science B.V. All rights reserved.

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http://hdl.handle.net/10453/26186