Next Article in Journal
Two New Strategies for Pricing Freight Options by Means of a Valuation PDE and by Functional Bounds
Next Article in Special Issue
Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network
Previous Article in Journal
Generating of Nonisospectral Integrable Hierarchies via the Lie-Algebraic Recursion Scheme
Previous Article in Special Issue
Application of Mixed Sampling to Real Life Data: A Case Study on Socio-Economic Determinants by Using SEM and CFA Techniques
Open AccessArticle

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

1
Departamento de Matemáticas, Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510 Ciudad de México, Mexico
2
Departamento de Matemáticas y Física, Cátedra CONACyT, Universidad Autónoma de Aguascalientes, 20130 Aguascalientes, Mexico
3
Departamento de Matemáticas, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 622; https://doi.org/10.3390/math8040622
Received: 15 March 2020 / Revised: 13 April 2020 / Accepted: 15 April 2020 / Published: 17 April 2020
(This article belongs to the Special Issue Statistics 2020)
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. View Full-Text
Keywords: Bayesian analysis; conditional independence; hidden Markov model; measurement error; misclassification; monotone continuous process; ordinal response Bayesian analysis; conditional independence; hidden Markov model; measurement error; misclassification; monotone continuous process; ordinal response
Show Figures

Figure 1

MDPI and ACS Style

Naranjo, L.; Esparza, L.J.R.; Pérez, C.J. A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process. Mathematics 2020, 8, 622.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop