dc.contributor.author |
Wang, Yong |
en |
dc.date.accessioned |
2012-03-14T19:12:48Z |
en |
dc.date.issued |
2010 |
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dc.identifier.citation |
Statistics and Computing 20(1):75-86 2010 |
en |
dc.identifier.issn |
0960-3174 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/14339 |
en |
dc.description.abstract |
Three general algorithms that use different strategies are proposed for computing the maximum likelihood estimate of a semiparametric mixture model. They seek to maximize the likelihood function by, respectively, alternating the parameters, profiling the likelihood and modifying the support set. All three algorithms make a direct use of the recently proposed fast and stable constrained Newton method for computing the nonparametric maximum likelihood of a mixing distribution and employ additionally an optimization algorithm for unconstrained problems. The performance of the algorithms is numerically investigated and compared for solving the Neyman-Scott problem, overcoming overdispersion in logistic regression models and fitting two-level mixed effects logistic regression models. Satisfactory results have been obtained. |
en |
dc.language |
EN |
en |
dc.publisher |
Springer |
en |
dc.relation.ispartofseries |
Statistics and Computing |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from: http://www.sherpa.ac.uk/romeo/issn/0960-3174/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.subject |
Constrained optimization |
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dc.subject |
Maximum likelihood computation |
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dc.subject |
Mixed effects |
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dc.subject |
Neyman-Scott problem |
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dc.subject |
Profile likelihood |
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dc.subject |
Semiparametric mixture |
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dc.subject |
GENERALIZED LINEAR-MODELS |
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dc.subject |
MIXING DISTRIBUTION |
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dc.subject |
CENSORED-DATA |
en |
dc.subject |
EM ALGORITHM |
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dc.subject |
COMPONENTS |
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dc.subject |
REGRESSION |
en |
dc.title |
Maximum likelihood computation for fitting semiparametric mixture models |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.1007/s11222-009-9117-z |
en |
pubs.issue |
1 |
en |
pubs.begin-page |
75 |
en |
pubs.volume |
20 |
en |
dc.rights.holder |
Copyright: Springer |
en |
pubs.end-page |
86 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
90228 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Statistics |
en |
pubs.record-created-at-source-date |
2010-09-01 |
en |