Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Bayesian ordinal probit semiparametric regression models: KNHANES 2016 data analysis of the relationship between smoking behavior and coffee intake

Full metadata record
DC Field Value Language
dc.contributor.authorLee, Dasom-
dc.contributor.authorLee, Eunji-
dc.contributor.authorJo, Seogil-
dc.contributor.authorChoi, Taeryeon-
dc.date.accessioned2021-12-10T04:41:23Z-
dc.date.available2021-12-10T04:41:23Z-
dc.date.created2021-08-30-
dc.date.issued2020-02-
dc.identifier.issn1225-066X-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/130725-
dc.description.abstractThis paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherKOREAN STATISTICAL SOC-
dc.subjectINJURY SEVERITY-
dc.titleBayesian ordinal probit semiparametric regression models: KNHANES 2016 data analysis of the relationship between smoking behavior and coffee intake-
dc.typeArticle-
dc.contributor.affiliatedAuthorChoi, Taeryeon-
dc.identifier.doi10.5351/KJAS.2020.33.1.025-
dc.identifier.wosid000531013000003-
dc.identifier.bibliographicCitationKOREAN JOURNAL OF APPLIED STATISTICS, v.33, no.1, pp.25 - 46-
dc.relation.isPartOfKOREAN JOURNAL OF APPLIED STATISTICS-
dc.citation.titleKOREAN JOURNAL OF APPLIED STATISTICS-
dc.citation.volume33-
dc.citation.number1-
dc.citation.startPage25-
dc.citation.endPage46-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.identifier.kciidART002564366-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusINJURY SEVERITY-
dc.subject.keywordAuthorBSAR-
dc.subject.keywordAuthorGaussian process-
dc.subject.keywordAuthorKNHANES data-
dc.subject.keywordAuthorMarkov chain Monte Carlo-
dc.subject.keywordAuthorOrdinal probit-
dc.subject.keywordAuthorSemiparametric regression-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Political Science & Economics > Department of Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Choi, Tae ryon photo

Choi, Tae ryon
정경대학 (통계학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE