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

Authors
Lee, DasomLee, EunjiJo, SeogilChoi, Taeryeon
Issue Date
2월-2020
Publisher
KOREAN STATISTICAL SOC
Keywords
BSAR; Gaussian process; KNHANES data; Markov chain Monte Carlo; Ordinal probit; Semiparametric regression
Citation
KOREAN JOURNAL OF APPLIED STATISTICS, v.33, no.1, pp.25 - 46
Indexed
KCI
Journal Title
KOREAN JOURNAL OF APPLIED STATISTICS
Volume
33
Number
1
Start Page
25
End Page
46
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/130725
DOI
10.5351/KJAS.2020.33.1.025
ISSN
1225-066X
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.
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정경대학 (통계학과)
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