{"id":562,"date":"2017-09-28T03:39:24","date_gmt":"2017-09-28T03:39:24","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=562"},"modified":"2017-09-28T03:40:01","modified_gmt":"2017-09-28T03:40:01","slug":"hierarchical-bayesian-approach-estimation-monotone-hazard-rates-random-right-censorship-model","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/hierarchical-bayesian-approach-estimation-monotone-hazard-rates-random-right-censorship-model\/","title":{"rendered":"A hierarchical Bayesian approach to the estimation of monotone hazard rates in the random right censorship model"},"content":{"rendered":"
Here we study hierarchical Bayesian estimation of a monotone hazard rate for
\nboth complete and randomly right censored data. We propose two methods
\nof computation: Monte-Carlo importance sampling and Laplace approximation
\ntechniques. These methods are computationally simple and easily implemented
\non complex hazard functions. They are compared in simulation studies with uncensored
\nand censored data and the methodology is illustrated on two interesting
\ndata sets.<\/p>\n
<\/p>\n