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Article: Zero-one–inflated simplex regression models for the analysis of continuous proportion data
Title | Zero-one–inflated simplex regression models for the analysis of continuous proportion data |
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Authors | |
Keywords | continuous proportion data MM algorithm simplex distribution zero-one-inflated beta model zero-one-inflated simplex model |
Issue Date | 2020 |
Publisher | International 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? |
Abstract | Continuous 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 Identifier | http://hdl.handle.net/10722/287722 |
ISSN | 2023 Impact Factor: 0.3 2023 SCImago Journal Rankings: 0.273 |
DC Field | Value | Language |
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dc.contributor.author | Liu, P | - |
dc.contributor.author | Yuen, KC | - |
dc.contributor.author | Wu, LC | - |
dc.contributor.author | Tian, GL | - |
dc.contributor.author | Li, T | - |
dc.date.accessioned | 2020-10-05T12:02:18Z | - |
dc.date.available | 2020-10-05T12:02:18Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Statistics and its Interface, 2020, v. 13 n. 2, p. 193-208 | - |
dc.identifier.issn | 1938-7989 | - |
dc.identifier.uri | http://hdl.handle.net/10722/287722 | - |
dc.description.abstract | Continuous 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.language | eng | - |
dc.publisher | International Press. The Journal's web site is located at http://www.intlpress.com/SII | - |
dc.relation.ispartof | Statistics and its Interface | - |
dc.rights | Statistics and its Interface. Copyright © International Press. | - |
dc.subject | continuous proportion data | - |
dc.subject | MM algorithm | - |
dc.subject | simplex distribution | - |
dc.subject | zero-one-inflated beta model | - |
dc.subject | zero-one-inflated simplex model | - |
dc.title | Zero-one–inflated simplex regression models for the analysis of continuous proportion data | - |
dc.type | Article | - |
dc.identifier.email | Yuen, KC: kcyuen@hku.hk | - |
dc.identifier.authority | Yuen, KC=rp00836 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.4310/SII.2020.v13.n2.a5 | - |
dc.identifier.scopus | eid_2-s2.0-85079523609 | - |
dc.identifier.hkuros | 315605 | - |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 193 | - |
dc.identifier.epage | 208 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1938-7989 | - |