A Bayesian approach for misclassified ordinal response data. Issue 12 (10th September 2019)

• Record Type: Journal Article
• Authors: Naranjo, Lizbeth; Pérez, Carlos J.; Martín, Jacinto; Mutsvari, Timothy; Lesaffre, Emmanuel
• Is Part Of: Journal of applied statistics. Volume 46:Issue 12(2019)
• Page Start: 2198
• Page End: 2215
• Publication Date: 2019-09-10
• Subjects: Bayesian analysis -- data augmentation -- Markov chain Monte Carlo methods -- misclassification -- ordinal regression model; 62F15 -- 62J99 -- 62P10; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2019.1582613
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Motivated by a longitudinal oral health study, the Signal-Tandmobiel ® study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulation-based example and the analysis of the motivating study.