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Article: Statistical inference for truncated Dirichlet distribution and its application in misclassification

TitleStatistical inference for truncated Dirichlet distribution and its application in misclassification
Authors
KeywordsBayesian Inference
Conditional Distribution Method
Constrained Mle
Experimental Design
Gibbs Sampler
Screening Test
Sensitivity
Specificity
Issue Date2000
PublisherWiley - V C H Verlag GmbH & Co KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournal
Citation
Biometrical Journal, 2000, v. 42 n. 8, p. 1053-1068 How to Cite?
AbstractThis paper is concerned with the statistical inference of a truncated Dirichlet distribution (TDD) arising in the general context of misclassified multinomial models (such as medical screening or diagnostic tests) and experimental design with mixtures. By employing the conditional distribution method, we offer a generating procedure for the TDD. Alternatively, a sampling-based approach using the Gibbs sampler was provided as a means for developing the posterior moments of interest. Finding the mode of a TDD is equivalent to extracting the constrained maximum likelihood estimate (MLE) of parameter vector in a multinomial model. Based upon a theoretic result, we propose an algorithm to calculate the constrained MLE. Applications in misclassification are presented.
Persistent Identifierhttp://hdl.handle.net/10722/172385
ISSN
2015 Impact Factor: 0.683
2015 SCImago Journal Rankings: 0.784
References

 

DC FieldValueLanguage
dc.contributor.authorFang, KTen_US
dc.contributor.authorGeng, Zen_US
dc.contributor.authorTian, GLen_US
dc.date.accessioned2012-10-30T06:22:16Z-
dc.date.available2012-10-30T06:22:16Z-
dc.date.issued2000en_US
dc.identifier.citationBiometrical Journal, 2000, v. 42 n. 8, p. 1053-1068en_US
dc.identifier.issn0323-3847en_US
dc.identifier.urihttp://hdl.handle.net/10722/172385-
dc.description.abstractThis paper is concerned with the statistical inference of a truncated Dirichlet distribution (TDD) arising in the general context of misclassified multinomial models (such as medical screening or diagnostic tests) and experimental design with mixtures. By employing the conditional distribution method, we offer a generating procedure for the TDD. Alternatively, a sampling-based approach using the Gibbs sampler was provided as a means for developing the posterior moments of interest. Finding the mode of a TDD is equivalent to extracting the constrained maximum likelihood estimate (MLE) of parameter vector in a multinomial model. Based upon a theoretic result, we propose an algorithm to calculate the constrained MLE. Applications in misclassification are presented.en_US
dc.languageengen_US
dc.publisherWiley - V C H Verlag GmbH & Co KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournalen_US
dc.relation.ispartofBiometrical Journalen_US
dc.subjectBayesian Inferenceen_US
dc.subjectConditional Distribution Methoden_US
dc.subjectConstrained Mleen_US
dc.subjectExperimental Designen_US
dc.subjectGibbs Sampleren_US
dc.subjectScreening Testen_US
dc.subjectSensitivityen_US
dc.subjectSpecificityen_US
dc.titleStatistical inference for truncated Dirichlet distribution and its application in misclassificationen_US
dc.typeArticleen_US
dc.identifier.emailTian, GL: gltian@hku.hken_US
dc.identifier.authorityTian, GL=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0034551876en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034551876&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume42en_US
dc.identifier.issue8en_US
dc.identifier.spage1053en_US
dc.identifier.epage1068en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridFang, KT=7102880697en_US
dc.identifier.scopusauthoridGeng, Z=7101959672en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US

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