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Article: An assembly and decomposition approach for constructing separable minorizing functions in a class of MM algorithms

TitleAn assembly and decomposition approach for constructing separable minorizing functions in a class of MM algorithms
Authors
KeywordsCase II interval censored data
Complemental assembly
Compound Zero-inflated
Transmission Tomography
Truncation
Issue Date2019
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2019, v. 29 n. 2, p. 963-984 How to Cite?
AbstractThe minorization–maximization (MM) principle provides a powerful tool for optimization in statistical applications. A challenging and subjective issue in developing an MM algorithm is to construct an appropriate minorizing function. For numerical convenience, our (AD) approach to constructing the minorizing function as the sum of separable univariate functions yields general class of MM algorithms. We employ the assembly technique (A-technique) and the decomposition technique (D-technique). The A-technique introduces a bank of complemental assembly functions which are often the building blocks of various MM algorithms. The D-technique decomposes the objective function into three parts and separately minorizes them. We illustrate the utility of the proposed approach in multiple applications. Numerical experiments demonstrate its advantages.
Persistent Identifierhttp://hdl.handle.net/10722/259505
ISSN
2019 Impact Factor: 0.968
2015 SCImago Journal Rankings: 2.292

 

DC FieldValueLanguage
dc.contributor.authorTian, G-
dc.contributor.authorHuang, X-
dc.contributor.authorXu, J-
dc.date.accessioned2018-09-03T04:08:55Z-
dc.date.available2018-09-03T04:08:55Z-
dc.date.issued2019-
dc.identifier.citationStatistica Sinica, 2019, v. 29 n. 2, p. 963-984-
dc.identifier.issn1017-0405-
dc.identifier.urihttp://hdl.handle.net/10722/259505-
dc.description.abstractThe minorization–maximization (MM) principle provides a powerful tool for optimization in statistical applications. A challenging and subjective issue in developing an MM algorithm is to construct an appropriate minorizing function. For numerical convenience, our (AD) approach to constructing the minorizing function as the sum of separable univariate functions yields general class of MM algorithms. We employ the assembly technique (A-technique) and the decomposition technique (D-technique). The A-technique introduces a bank of complemental assembly functions which are often the building blocks of various MM algorithms. The D-technique decomposes the objective function into three parts and separately minorizes them. We illustrate the utility of the proposed approach in multiple applications. Numerical experiments demonstrate its advantages.-
dc.languageeng-
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/-
dc.relation.ispartofStatistica Sinica-
dc.subjectCase II interval censored data-
dc.subjectComplemental assembly-
dc.subjectCompound Zero-inflated-
dc.subjectTransmission Tomography-
dc.subjectTruncation-
dc.titleAn assembly and decomposition approach for constructing separable minorizing functions in a class of MM algorithms-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5705/ss.202016.0488-
dc.identifier.scopuseid_2-s2.0-85072074668-
dc.identifier.hkuros289174-
dc.identifier.volume29-
dc.identifier.issue2-
dc.identifier.spage963-
dc.identifier.epage984-
dc.publisher.placeTaiwan, Republic of China-

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