Browsing by Author "Yadav, Abhimanyu S."

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    ASSESSMENT OF GENERALIZED LIFETIME PERFORMANCE INDEX FOR LINDLEY DISTRIBUTION USING PROGRESSIVE TYPE-II SAMPLES
    (Gnedenko Forum, 2023) Yadav, Abhimanyu S.; Saha, Mahendra; Bhattacharya, Amartya; Gupta, Arindam
    A meaningful subject of discourse in manufacturing industries is the assessment of the lifetime performance index. In manufacturing industries, the lifetime performance index is used to measure the performance of the product. A generalized lifetime performance index (GLPI) is defined by taking into consideration the median of the process measurement when the lifetime of products follow a parametric distribution may serve better the need of quality engineers and scientists in industry. The present study constructs various point estimators of the GLPI based on progressive type II right censored data for the Lindley distributed lifetime in both classical and Bayesian setup. We perform Monte Carlo simulations to compare the performances of the maximum likelihood and Bayes estimates with a gamma prior of CY (L) under progressive type-II right censoring scheme. Finally, the validity of the model is adjudged through analysis of a data set. � 2023, Gnedenko Forum. All rights reserved.
  • Thumbnail Image
    Publication
    Confidence intervals for the reliability characteristics via different estimation methods for the power Lindley model
    (Gnedenko Forum, 2022) Yadav, Abhimanyu S.; Vishwakarma, P.K.; Bakouch, H.S.; Kumar, Upendra; Chauhan, S.
    In this article, classical and Bayes interval estimation procedures have been discussed for the reliability characteristics, namely mean time to system failure, reliability function, and hazard function for the power Lindley model and its special case. In the classical part, maximum likelihood estimation, maximum product spacing estimation are discussed to estimate the reliability characteristics. Since the computation of the exact confidence intervals for the reliability characteristics is not directly possible, then, using the large sample theory, the asymptotic confidence interval is constructed using the above-mentioned classical estimation methods. Further, the bootstrap (standard-boot, percentile-boot, students t-boot) confidence intervals are also obtained. Next, Bayes estimators are derived with a gamma prior using squared error loss function and linex loss function. The Bayes credible intervals for the same characteristics are constructed using simulated posterior samples. The obtained estimators are evaluated by the Monte Carlo simulation study in terms of mean square error, average width, and coverage probabilities. A real-life example has also been illustrated for the application purpose. � 2022 Reliability: Theory and Applications. All rights reserved.