A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process

Abstract

A Bayesian approach was developed, tested, and applied to model ordinal response data in monotone non-decreasing processes with measurement errors. An inhomogeneous hidden Markov model with continuous state-space was considered to incorporate measurement errors in the categorical response at the same time that the non-decreasing patterns were kept. The computational difficulties were avoided by including latent variables that allowed implementing an efficient Markov chain Monte Carlo method. A simulation-based analysis was carried out to validate the approach, whereas the proposed approach was applied to analyze aortic aneurysm progression data.

Keywords

• Bayesian analysis
• conditional independence
• hidden Markov model
• measurement error
• misclassification
• monotone continuous process