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

Lizbeth Naranjo, Luz Judith R. Esparza and Carlos J. Pérez
Additional contact information
Lizbeth Naranjo: Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
Luz Judith R. Esparza: Departamento de Matemáticas y Física, Cátedra CONACyT, Universidad Autónoma de Aguascalientes, 20130 Aguascalientes, Mexico
Carlos J. Pérez: Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain

Mathematics, 2020, vol. 8, issue 4, 1-12

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; ordinal response (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/4/622/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/4/622/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:4:p:622-:d:346729

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().