Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10662/21125
Títulos: A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process
Autores/as: Naranjo, Lizbeth
Esparza, Luz Judith R.
Pérez Sánchez, Carlos Javier
Palabras clave: Bayesian analysis;Conditional independence;Hidden Markov model;Measurement error;Misclassification;Monotone continuous process;Ordinal response;Análisis bayesiano;Independencia condicional;Error de medición;Modelo oculto de Markov;Modelo de respuesta ordinal
Fecha de publicación: 2020
Editor/a: MDPI
Resumen: 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.
URI: http://hdl.handle.net/10662/21125
ISSN: 2227-7390
DOI: 10.3390/math8040622
Colección:DMATE - Artículos

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