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Article: Unified generalized iterative scaling and its applications

TitleUnified generalized iterative scaling and its applications
Authors
Gao, WShi, NZTang, MLFu, LTian, G
Issue Date2010
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 2010, v. 54 n. 4, p. 1066-1078 How to Cite?
DOI: http://dx.doi.org/10.1016/j.csda.2009.10.017
AbstractGeneralized iterative scaling (GIS) has become a popular method for getting the maximum likelihood estimates for log-linear models. It is basically a sequence of successive I-projections onto sets of probability vectors with some given linear combinations of probability vectors. However, when a sequence of successive I-projections are applied onto some closed and convex sets (e.g., marginal stochastic order), they may not lead to the actual solution. In this manuscript, we present a unified generalized iterative scaling (UGIS) and the convergence of this algorithm to the optimal solution is shown. The relationship between the UGIS and the constrained maximum likelihood estimation for log-linear models is established. Applications to constrained Poisson regression modeling and marginal stochastic order are used to demonstrate the proposed UGIS. Crown Copyright © 2009.
Persistent Identifierhttp://hdl.handle.net/10722/125410
ISSN
0167-9473
2023 Impact Factor: 1.5
2023 SCImago Journal Rankings: 1.008
ISI Accession Number ID
WOS:000274574600024
Funding AgencyGrant Number
NSFCNSFC: 10701021
NSFC: 10931002
NSFC: 10828102
NENU-STC07001
Hong Kong Special Administrative RegionKBU261508
Hong Kong Baptist universityFRG2/08-09/066
Funding Information:

The authors thank the associate editor and two anonymous reviewers for helpful comments and suggestions on an earlier version of this article. This work was supported by NSFC: 10701021, NSFC: 10931002, NSFC: 10828102 and NENU-STC07001. M.L. Tang's research was fully supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region (Project No. KBU261508) and the Hong Kong Baptist university Grant FRG2/08-09/066.

References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorGao, Wen_HK
dc.contributor.authorShi, NZen_HK
dc.contributor.authorTang, MLen_HK
dc.contributor.authorFu, Len_HK
dc.contributor.authorTian, Gen_HK
dc.date.accessioned2010-10-31T11:29:49Z-
dc.date.available2010-10-31T11:29:49Z-
dc.date.issued2010en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 2010, v. 54 n. 4, p. 1066-1078en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125410-
dc.description.abstractGeneralized iterative scaling (GIS) has become a popular method for getting the maximum likelihood estimates for log-linear models. It is basically a sequence of successive I-projections onto sets of probability vectors with some given linear combinations of probability vectors. However, when a sequence of successive I-projections are applied onto some closed and convex sets (e.g., marginal stochastic order), they may not lead to the actual solution. In this manuscript, we present a unified generalized iterative scaling (UGIS) and the convergence of this algorithm to the optimal solution is shown. The relationship between the UGIS and the constrained maximum likelihood estimation for log-linear models is established. Applications to constrained Poisson regression modeling and marginal stochastic order are used to demonstrate the proposed UGIS. Crown Copyright © 2009.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.titleUnified generalized iterative scaling and its applicationsen_HK
dc.typeArticleen_HK
dc.identifier.emailTian, G: gltian@hku.hken_HK
dc.identifier.authorityTian, G=rp00789en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2009.10.017en_HK
dc.identifier.scopuseid_2-s2.0-73149103681en_HK
dc.identifier.hkuros178714en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-73149103681&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume54en_HK
dc.identifier.issue4en_HK
dc.identifier.spage1066en_HK
dc.identifier.epage1078en_HK
dc.identifier.isiWOS:000274574600024-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridGao, W=15848238700en_HK
dc.identifier.scopusauthoridShi, NZ=7004451232en_HK
dc.identifier.scopusauthoridTang, ML=7401974011en_HK
dc.identifier.scopusauthoridFu, L=35307037500en_HK
dc.identifier.scopusauthoridTian, G=25621549400en_HK
dc.identifier.citeulike6037585-
dc.identifier.issnl0167-9473-

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