Bayesian ordinal probit semiparametric regression models: KNHANES 2016 data analysis of the relationship between smoking behavior and coffee intake
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Dasom | - |
dc.contributor.author | Lee, Eunji | - |
dc.contributor.author | Jo, Seogil | - |
dc.contributor.author | Choi, Taeryeon | - |
dc.date.accessioned | 2021-12-10T04:41:23Z | - |
dc.date.available | 2021-12-10T04:41:23Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.issn | 1225-066X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/130725 | - |
dc.description.abstract | This 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.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.subject | INJURY SEVERITY | - |
dc.title | Bayesian ordinal probit semiparametric regression models: KNHANES 2016 data analysis of the relationship between smoking behavior and coffee intake | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Taeryeon | - |
dc.identifier.doi | 10.5351/KJAS.2020.33.1.025 | - |
dc.identifier.wosid | 000531013000003 | - |
dc.identifier.bibliographicCitation | KOREAN JOURNAL OF APPLIED STATISTICS, v.33, no.1, pp.25 - 46 | - |
dc.relation.isPartOf | KOREAN JOURNAL OF APPLIED STATISTICS | - |
dc.citation.title | KOREAN JOURNAL OF APPLIED STATISTICS | - |
dc.citation.volume | 33 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 25 | - |
dc.citation.endPage | 46 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002564366 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | INJURY SEVERITY | - |
dc.subject.keywordAuthor | BSAR | - |
dc.subject.keywordAuthor | Gaussian process | - |
dc.subject.keywordAuthor | KNHANES data | - |
dc.subject.keywordAuthor | Markov chain Monte Carlo | - |
dc.subject.keywordAuthor | Ordinal probit | - |
dc.subject.keywordAuthor | Semiparametric regression | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.