A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.
Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regressio...
| Publicado en: | Statistical Methods in Medical Research Vol. 27; no. 4; pp. 1024 - 1045 |
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| Autores principales: | , , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Apr2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128289563&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128289563 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Apr2018 vid: 27 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 128289563 128289563 NLM27230127 128289563 10.1177/0962280216650699 NLM27230127 128289563 ppf: 1024 ppct: 21 formats: tig: atl: A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression. aug: au: Fang Liu Eugenio, Evercita C. Liu, Fang affil: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA sug: subj: Regression Probability Models, Statistical Research, Medical ab: Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed. pubtype: Academic Journal doctype: equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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