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Bayesian analysis of head and neck cancer data using generalized inverse Lindley stress–strength reliability model

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  • Vikas Kumar Sharma

Abstract

In this article, we present the analysis of head and neck cancer data using generalized inverse Lindley stress–strength reliability model. We propose Bayes estimators for estimating P(X > Y), when X and Y represent survival times of two groups of cancer patients observed under different therapies. The X and Y are assumed to be independent generalized inverse Lindley random variables with common shape parameter. Bayes estimators are obtained under the considerations of symmetric and asymmetric loss functions assuming independent gamma priors. Since posterior becomes complex and does not possess closed form expressions for Bayes estimators, Lindley’s approximation and Markov Chain Monte Carlo techniques are utilized for Bayesian computation. An extensive simulation experiment is carried out to compare the performances of Bayes estimators with the maximum likelihood estimators on the basis of simulated risks. Asymptotic, bootstrap, and Bayesian credible intervals are also computed for the P(X > Y).

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  • Vikas Kumar Sharma, 2018. "Bayesian analysis of head and neck cancer data using generalized inverse Lindley stress–strength reliability model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(5), pages 1155-1180, March.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:5:p:1155-1180
    DOI: 10.1080/03610926.2017.1316858
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    Cited by:

    1. Varun Agiwal, 2023. "Bayesian Estimation of Stress Strength Reliability from Inverse Chen Distribution with Application on Failure Time Data," Annals of Data Science, Springer, vol. 10(2), pages 317-347, April.
    2. Indrajeet Kumar & Kapil Kumar, 2022. "On estimation of $$P(V," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 189-202, February.

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