Constant Hazard Function Models with a Change Point: A Bayesian Analysis Using Markov Chain Monte Carlo Methods

DOI<link itemprop=identifier href="https://doi.org/10.1002/(SICI)1521-4036(199809)40:5<543::AID-BIMJ543>3.0.CO;2-B" /><543::AID-BIMJ543>3.0.CO;2-B 10.1002/(SICI)1521-4036(199809)40:5<543::AID-BIMJ543>3.0.CO;2-BzbMath0911.62022OpenAlexW1968460457MaRDI QIDQ4224690

Selene Loibel, Jorge Alberto Achcar

Publication date: 10 March 1999

Full work available at URL: https://doi.org/10.1002/(sici)1521-4036(199809)40:5<543::aid-bimj543>3.0.co;2-b

zbMATH Keywords

Metropolis algorithmGibbs samplingchange-pointconstant hazard

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Probabilistic methods, stochastic differential equations (65C99)

Related Items (5)

Inference on a Sharp Jump in Hazard Rate: A Review ⋮ On computing estimates of a change-point in the Weibull regression hazard model ⋮ Multiple Change-Point Detection in Piecewise Exponential Hazard Regression Models with Long-Term Survivors and Right Censoring ⋮ A new Bayes estimate of the change point in the hazard function ⋮ Modified estimators for the change point in hazard function

This page was built for publication: Constant Hazard Function Models with a Change Point: A Bayesian Analysis Using Markov Chain Monte Carlo Methods