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Bayesian Estimation of Stress Strength Reliability from Inverse Chen Distribution with Application on Failure Time Data

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  • Varun Agiwal

    (Jawaharlal Nehru Medical College)

Abstract

In this article, we develop Bayesian estimation procedure for estimating the stress strength reliability R = P [X > Y ] when X (strength) and Y (stress) are the inverse Chen random variables. First, we study some statistical properties of the inverse Chen distribution such as quantiles, mode, stochastic ordering, entropy measure, order statistics and stress strength reliability. Then, we estimate the stress strength parameters and R using maximum likelihood and Bayesian estimations. A symmetric (squared error loss) and an asymmetric (entropy loss) loss functions are considered for Bayesian estimation under the assumption of gamma prior. Since, joint posterior distribution of the model parameters and R involve multiple integrations and have complex form. So, we do not get analytical solution without using any numerical techniques. Therefore, we propose to use Lindley’s approximation and Markov chain Monte Carlo techniques for Bayesian computation. A simulation study is carried out for the proposed Bayes estimators of unknown parameters and compared with the maximum likelihood estimator on the basis of mean squared error. Finally, an empirical illustration based on failure time data is presented to demonstrate the applicability of inverse Chen stress strength model.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:2:d:10.1007_s40745-020-00313-w
    DOI: 10.1007/s40745-020-00313-w
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    References listed on IDEAS

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    1. M. S. Eliwa & M. El-Morshedy, 2019. "Bivariate Gumbel-G Family of Distributions: Statistical Properties, Bayesian and Non-Bayesian Estimation with Application," Annals of Data Science, Springer, vol. 6(1), pages 39-60, March.
    2. 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.
    3. Chen, Zhenmin, 2000. "A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function," Statistics & Probability Letters, Elsevier, vol. 49(2), pages 155-161, August.
    4. Manoj Kumar & Anurag Pathak & Sukriti Soni, 2019. "Bayesian Inference for Rayleigh Distribution Under Step-Stress Partially Accelerated Test with Progressive Type-II Censoring with Binomial Removal," Annals of Data Science, Springer, vol. 6(1), pages 117-152, March.
    5. Farhad Yousaf & Sajid Ali & Ismail Shah, 2019. "Statistical Inference for the Chen Distribution Based on Upper Record Values," Annals of Data Science, Springer, vol. 6(4), pages 831-851, December.
    6. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    7. Teena Goyal & Piyush K. Rai & Sandeep K. Maurya, 2020. "Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 7(2), pages 307-345, June.
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