Interfailure Data with Constant Hazard Function in the Presence of Change-Points

Jorge Alberto  Achcar; Selene Loibel; Marinho G. Andrade

doi:10.57805/revstat.v5i2.49

Interfailure Data with Constant Hazard Function in the Presence of Change-Points

Authors

Jorge Alberto Achcar Universidade Federal de São Carlos
Selene Loibel Universidade Federal de São Carlos
Marinho G. Andrade Universidade Federal de São Carlos

DOI:

https://doi.org/10.57805/revstat.v5i2.49

Keywords:

constant hazard, change-points, Gibbs sampling, MCMC algorithms

Abstract

Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8].

Downloads

REVSTAT2007v5n2_4

Published

2007-06-29

How to Cite

Achcar , J. A. ., Loibel , S., & G. Andrade , M. (2007). Interfailure Data with Constant Hazard Function in the Presence of Change-Points. REVSTAT-Statistical Journal, 5(2), 209–226. https://doi.org/10.57805/revstat.v5i2.49