Acceptance sampling plans based on truncated life tests for Rama distribution

Author:

Al-Omari Amer,Al-Nasser Amjad,Ciavolino EnricoORCID

Abstract

Purpose Lifetime data are used in many different applied sciences, like biomedicine, engineering, insurance and finance and others. The purpose of this paper is to develop a new acceptance sampling plans for Rama distribution when the mean lifetime test is truncated at a pre-determined time. The minimum sample sizes required to assert the specified life mean is obtained for a given customer’s risk. The operating characteristic function values of the sampling plans and producer’s risk are calculated. Design/methodology/approach The results are illustrated using numerical examples and a real data set is considered to illustrate the performance of the suggested acceptance sampling plans and how it can be used for the industry applications. Findings This paper shows a new acceptance sampling plans based on Rama distribution in the particular case when the mean life time test is truncated. Originality/value The results calculated in this paper demonstrate the differences between OC values for different distributions taken into account. In particular, OC values of Rama distribution are found to be less than the proposed distribution counterparts.

Publisher

Emerald

Subject

Strategy and Management,General Business, Management and Accounting

Reference30 articles.

1. Acceptance sampling plan based on truncated life tests for exponentiated Frechet distribution;Journal of Statistics and Management Systems,2013

2. On using the median ranked set sampling for developing reliability test plans under generalized exponential distribution;Pakistan Journal of Statistics and Operation Research,2017

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4. Double acceptance sampling plan for time truncated life tests based on transmuted generalized inverse Weibull distribution;Journal of Statistics Applications and Probability,2017

5. Economic design of acceptance sampling plans for truncated life tests using three-parameter Lindley distribution;Journal of Modern Applied Statistical Methods,2018

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