On use of the EM algorithm for penalized likelihood estimation

Green, PJ

On use of the EM algorithm for penalized likelihood estimation

Green, PJ

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Publisher:: Blackwell Publ Ltd
Publication Type:: Journal Article
Citation:: Journal of The Royal Statistical Society Series B-methodological, 1990, 52 (3), pp. 443 - 452
Issue Date:: 1990-01

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Field	Value	Language
dc.contributor.author	Green, PJ https://orcid.org/0000-0002-4367-4756	en_US
dc.date.issued	1990-01	en_US
dc.identifier.citation	Journal of The Royal Statistical Society Series B-methodological, 1990, 52 (3), pp. 443 - 452	en_US
dc.identifier.issn	1369-7412	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14550
dc.description.abstract	The EM algorithmis a popular approach to maximuml ikelihoode stimationb ut has not been muchu sed forp enalizedl ikelihoodo r maximuma posteriori estimation.T his paper discussesp ropertieos f theE M algorithmin suchc ontextsc, oncentratinogn rateso f convergence, and presentsa n alternativet hati s usuallym ore practicala nd convergesa t least as quickly	en_US
dc.publisher	Blackwell Publ Ltd	en_US
dc.relation.ispartof	Journal of The Royal Statistical Society Series B-methodological	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	On use of the EM algorithm for penalized likelihood estimation	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	52	en_US
utslib.location.activity	ISI:A1990ED45700002	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1403 Econometrics	en_US
dc.location.activity	ISI:A1990ED45700002	en_US
dc.location.activity	Gold Coast, Australia
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.consider-herdc	false	en_US
pubs.issue	3	en_US
pubs.volume	52	en_US

Abstract:

The EM algorithmis a popular approach to maximuml ikelihoode stimationb ut has not been muchu sed forp enalizedl ikelihoodo r maximuma posteriori estimation.T his paper discussesp ropertieos f theE M algorithmin suchc ontextsc, oncentratinogn rateso f convergence, and presentsa n alternativet hati s usuallym ore practicala nd convergesa t least as quickly

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http://hdl.handle.net/10453/14550