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Article: Zero-one–inflated simplex regression models for the analysis of continuous proportion data

TitleZero-one–inflated simplex regression models for the analysis of continuous proportion data
Authors
Keywordscontinuous proportion data
MM algorithm
simplex distribution
zero-one-inflated beta model
zero-one-inflated simplex model
Issue Date2020
PublisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SII
Citation
Statistics and its Interface, 2020, v. 13 n. 2, p. 193-208 How to Cite?
AbstractContinuous data restricted in the closed unit interval [0,1] often appear in various fields. Neither the beta distribution nor the simplex distribution provides a satisfactory fitting for such data, since the densities of the two distributions are defined only in the open interval (0,1). To model continuous proportional data with excessive zeros and excessive ones, it is the first time that we propose a zero-one-inflated simplex (ZOIS) distribution, which can be viewed as a mixture of the Bernoulli distribution and the simplex distribution. Besides, we introduce a new minorization–maximization (MM) algorithm to calculate the maximum likelihood estimates (MLEs) of parameters in the simplex distribution without covariates. Likelihood-based inference methods for the ZOIS regression model are also provided. Some simulation studies are performed and the hospital stay data of Barcelona in 1988 and 1990 are analyzed to illustrate the proposed methods. The comparison between the ZOIS model and the zero-one-inflated beta (ZOIB) model is also presented.
Persistent Identifierhttp://hdl.handle.net/10722/287722
ISSN
2019 Impact Factor: 0.412
2015 SCImago Journal Rankings: 0.481

 

DC FieldValueLanguage
dc.contributor.authorLiu, P-
dc.contributor.authorYuen, KC-
dc.contributor.authorWu, LC-
dc.contributor.authorTian, GL-
dc.contributor.authorLi, T-
dc.date.accessioned2020-10-05T12:02:18Z-
dc.date.available2020-10-05T12:02:18Z-
dc.date.issued2020-
dc.identifier.citationStatistics and its Interface, 2020, v. 13 n. 2, p. 193-208-
dc.identifier.issn1938-7989-
dc.identifier.urihttp://hdl.handle.net/10722/287722-
dc.description.abstractContinuous data restricted in the closed unit interval [0,1] often appear in various fields. Neither the beta distribution nor the simplex distribution provides a satisfactory fitting for such data, since the densities of the two distributions are defined only in the open interval (0,1). To model continuous proportional data with excessive zeros and excessive ones, it is the first time that we propose a zero-one-inflated simplex (ZOIS) distribution, which can be viewed as a mixture of the Bernoulli distribution and the simplex distribution. Besides, we introduce a new minorization–maximization (MM) algorithm to calculate the maximum likelihood estimates (MLEs) of parameters in the simplex distribution without covariates. Likelihood-based inference methods for the ZOIS regression model are also provided. Some simulation studies are performed and the hospital stay data of Barcelona in 1988 and 1990 are analyzed to illustrate the proposed methods. The comparison between the ZOIS model and the zero-one-inflated beta (ZOIB) model is also presented.-
dc.languageeng-
dc.publisherInternational Press. The Journal's web site is located at http://www.intlpress.com/SII-
dc.relation.ispartofStatistics and its Interface-
dc.rightsStatistics and its Interface. Copyright © International Press.-
dc.subjectcontinuous proportion data-
dc.subjectMM algorithm-
dc.subjectsimplex distribution-
dc.subjectzero-one-inflated beta model-
dc.subjectzero-one-inflated simplex model-
dc.titleZero-one–inflated simplex regression models for the analysis of continuous proportion data-
dc.typeArticle-
dc.identifier.emailYuen, KC: kcyuen@hku.hk-
dc.identifier.authorityYuen, KC=rp00836-
dc.description.naturepostprint-
dc.identifier.doi10.4310/SII.2020.v13.n2.a5-
dc.identifier.scopuseid_2-s2.0-85079523609-
dc.identifier.hkuros315605-
dc.identifier.volume13-
dc.identifier.issue2-
dc.identifier.spage193-
dc.identifier.epage208-
dc.publisher.placeUnited States-

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