Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data

Chris Bambey Guure,Mohd Bakri Adam,Noor Akma Ibrahim

doi:10.1155/2013/849520

Chris Bambey Guure, Mohd Bakri Adam + Show 1 more

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https://doi.org/10.1155/2013/849520

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Abstract

Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.

Highlights

One of the features of survival data is censoring. e common one is right censoring and literature on it is well established
We have considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. is study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian
A real data application is illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters

Summary

Introduction

One of the features of survival data is censoring. e common one is right censoring and literature on it is well established. One of the features of survival data is censoring. E common one is right censoring and literature on it is well established. E focus of this study is on interval censoring, which presumably is more demanding than right censoring and, as a result, the approach developed for right censoring does not generally apply. Interval censoring has to do with a study subject of interest that is not under regular observation. It is not always possible to observe the failure or survival time of the subject. One only knows a range, that is, an interval, inside of which one can say the survival event has occurred. As stated by Turnbull [9], one could de ne an interval-censored observation as a union of several nonoverlapping windows or intervals

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