Reliability estimation for inverse Pareto lifetime model based on unified hybrid censored data (original) (raw)

Abstract

Censoring plays an important role in the reliability and life testing trials due to its cost optimality and time reduction properties. The unified hybrid censoring scheme is the combination of the generalized type-I and type-II hybrid censoring schemes. In this paper, our objective is to study the classical and Bayesian estimation methods of the parameter and reliability characteristics from the inverse Pareto lifetime model under the unified hybrid censoring scheme. In the classical estimation methods, the maximum likelihood and associated asymptotic confidence interval estimators are derived. In Bayesian estimation, the Bayes estimators under squared error loss function and the highest posterior density (HPD) credible intervals based on the informative and non-informative priors are developed. For the Bayesian computations, the Markov chain Monte Carlo techniques are used to compute Bayes and HPD credible interval estimates. A quantitative outcome of the objectives has been shown by a Monte Carlo simulation and with the help of a real-life application.

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Acknowledgements

The authors are thankful to the Editor-in-Chief, Associate Editor and two anonymous referees for their valuable suggestions, which led to the improved version of the earlier manuscript.

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Authors and Affiliations

1. Department of Statistics, Central University of Haryana, Mahendergarh, 123031, India
   Kapil Kumar
2. Department of Statistics, Kirori Mal College, New Delhi, Delhi, 110007, India
   Shrawan Kumar
3. Department of Statistics, Ramanujan College, New Delhi, Delhi, 110019, India
   Renu Garg
4. Department of Statistics, Central University of South Bihar, Gaya, 824236, India
   Indrajeet Kumar

Authors

1. Kapil Kumar
2. Shrawan Kumar
3. Renu Garg
4. Indrajeet Kumar

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Correspondence toIndrajeet Kumar.

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Kumar, K., Kumar, S., Garg, R. et al. Reliability estimation for inverse Pareto lifetime model based on unified hybrid censored data.Int J Syst Assur Eng Manag 15, 2473–2482 (2024). https://doi.org/10.1007/s13198-024-02265-3

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• Received: 20 May 2022
• Revised: 06 November 2023
• Accepted: 22 January 2024
• Published: 21 February 2024
• Version of record: 21 February 2024
• Issue date: June 2024
• DOI: https://doi.org/10.1007/s13198-024-02265-3

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