Classical and Bayesian inference of Cpy for generalized Lindley distributed quality characteristic. (18th August 2019)

• Record Type: Journal Article
• Authors: Saha, Mahendra; Dey, Sanku; Yadav, Abhimanyu Singh; Kumar, Sumit
• Is Part Of: Quality and reliability engineering international. Volume 35:Number 8(2019)
• Page Start: 2593
• Page End: 2611
• Publication Date: 2019-08-18
• Subjects: Bayes estimators -- bootstrap confidence interval -- maximum product of spacings estimators -- Monte Carlo simulation -- process capability index -- weighted least squares estimator; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2544
• Languages: English
• ISSNs: 0748-8017

Abstract

Abstract: The process capability index (PCI) is a quality control–related statistic mostly used in the manufacturing industry, which is used to assess the capability of some monitored process. It is of great significance to quality control engineers as it quantifies the relation between the actual performance of the process and the preset specifications of the product. Most of the traditional PCIs performed well when process follows the normal behaviour. However, using these traditional indices to evaluate a non‐normally distributed process often leads to inaccurate results. In this article, we consider a new PCI, C p y, suggested by Maiti et al, which can be used for normal as well as non‐normal random variables. This article addresses the different methods of estimation of the PCI C p y from both frequentist and Bayesian view points of generalized Lindley distribution suggested by Nadarajah et al. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, least square and weighted least square estimators, and maximum product of spacings estimators. Next, we consider Bayes estimation under squared error loss function using gamma priors for both shape and scale parameters for the considered model. We use Tierney and Kadane's method as well as Markov Chain Monte Carlo procedure to compute approximate Bayes estimates. Besides, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with highest posterior density credible intervals. Furthermore, Monte Carlo simulation study has been carried out to compare the performances of the classical and the Bayes estimates of C p y in terms of mean squared errors along with the average width and coverage probabilities. Finally, two real data sets have been analysed for illustrative purposes.