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Acceptance sampling inspection plan for the Lindley and power Lindley distributed quality characteristics

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Abstract

In this article, an acceptance sampling inspection plan is proposed when the quality characteristic follows the Lindley distribution. In this sampling plan, an exact approach has been introduced using the Lindley distributed quality characteristic while an approximate approach of acceptance sampling inspection has also been provided using the power Lindley distributed quality characteristic based on Box–Cox transformation for approximation to near normally distributed data. The plan parameters have been obtained by two approaches: the acceptable quality level (AQL) and the limiting quality level (LQL). For given producer’s risk and consumer’s risk, the plan parameters are reported according to various values of the AQL and LQL. Two real life examples are provided to illustrate the performance of the exact and approximated approach of the proposed acceptance sampling inspection plan.

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The link of the dataset used in this study is included within the article, and data set also provided in the article.

References

  • Al-Mutairi DK, Ghitany ME, Kundu D (2013) Inferences on stress-strength reliability from Lindley distributions. Commun Stat Theory Methods 42:1443–1463

    Article  MathSciNet  Google Scholar 

  • Al-Omari AI (2018) Acceptance sampling plans based on truncated life tests for Sushila distribution. J Math Fundam Sci 50(1):72–83

    Article  MathSciNet  Google Scholar 

  • Al-Omari A, Al-Nasser A, Ciavolino E (2019) Acceptance sampling plans based on truncated life tests for Rama distribution. Int J Qual Reliab Manag 36(7):1181–1191

    Article  Google Scholar 

  • Aslam M (2020) Product acceptance determinations using new nonparametric sign sampling plan. J Stat Manag Syst 23(8):1561–1570

    Google Scholar 

  • Aslam M (2021) Testing average wind speed using sampling plan for Weibull distribution under indeterminacy. Sci Rep 11(1):1–9

    Article  Google Scholar 

  • Aslam M, Kundu D, Jun C-H, Ahmad M (2011) Time truncated improved group acceptance sampling plans for generalized exponential distribution. J Test Eval 39:671–677

    Google Scholar 

  • Aslam M, Azam M, Jun CH (2017) A new sampling plan under the exponential distribution. Commun Stat Theory Methods 46(2):644–652

    Article  MathSciNet  Google Scholar 

  • Aslam M, Rao GS, Khan N (2021a) Single-stage and two-stage total failure-based group-sampling plans for the Weibull distribution under neutrosophic statistics. Complex Intell Syst 7(2):891–900

    Article  Google Scholar 

  • Aslam M, Jeyadurga P, Balamurali S, Azam M, AL-Marshadi A (2021b) Economic determination of modified multiple dependent state sampling plan under some lifetime distributions. J Math 1–13. https://doi.org/10.1155/2021/7470196

  • Balakrishnan N, Leiva V, Lopez J (2007) Acceptance sampling plans from truncated life tests based on the generalized Birnbaum–Saunders distribution. Commun Stat Simul Comput 36:643–656

    Article  MathSciNet  Google Scholar 

  • Box GEP, Cox DR (1964) An analysis of transformations. J Roy Stat Soc B 26:211–252

    MATH  Google Scholar 

  • Chen X, Tung Y (2003) Investigation of polynomial normal transform. Struct Saf 25:423–445

    Article  Google Scholar 

  • Draper J (1952) Properties of distributions resulting from certain simple transformations of the normal distribution. Biometrika 39:290–301

    Article  MathSciNet  Google Scholar 

  • Duncan AJ (1986) Quality control and industrial statistics. Richard D Irwin, Homewood

    MATH  Google Scholar 

  • Gasser T, Bacher P, Mocks J (1982) Transformations towards the normal distribution of broad band spectral parameters of the EEG. Electroencephalogr Clin Neurophysiol 53:119–124

    Article  Google Scholar 

  • Ghitany ME, Atieh B, Nadarajah S (2008) Lindley distribution and its application. Math Comput Simul 78:493–506

    Article  MathSciNet  Google Scholar 

  • Ghitany ME, Al-Mutairi DK, Balakrishnan N, Al-Enezi LJ (2013) Power Lindley distribution and associated inference. Comput Stat Data Anal 64:20–33

    Article  MathSciNet  Google Scholar 

  • Kantam RRL, Rosaiah K, Rao GS (2001) Acceptance sampling based on life tests: log-logistic models. J Appl Stat 28:121–128

    Article  MathSciNet  Google Scholar 

  • Kantam RRL, Srinivasa Rao G, Sriram G (2006) An economic reliability test plan: log-logistic distribution. J Appl Stat 33:291–296

    Article  MathSciNet  Google Scholar 

  • Khired AA, Aslam M, Dobbah SA (2021) Refined double sampling scheme with measures and application. Stat 10(1):e368

    Article  MathSciNet  Google Scholar 

  • Lawless JF (2003) Statistical models and methods for lifetime data. Wiley, New York

    MATH  Google Scholar 

  • Lindley DV (1958) Fiducial distributions and bayes’ theorem. J Roy Stat Soc 20:102–107

    MathSciNet  MATH  Google Scholar 

  • Lio YL, Tsai T-R, Wu S-J (2010) Acceptance sampling plans from truncated life tests based on the Burr type XII percentiles. J Chin Inst Ind Eng 27:270–280

    Google Scholar 

  • Mazucheli J, Achcar JA (2011) The Lindley distribution applied to competing risks lifetime data. Comput Methods Programs Biomed 104:188–92

    Article  Google Scholar 

  • Picciotto R (1970) Tensile fatigue characteristics of a sized polyester/viscose yarn and their effect on weaving performance. Master thesis, North Carolina State, USA

  • Rao GS (2013) Acceptance sampling plans for percentiles based on the Marshall–Olkin extended Lomax distribution. J Stat Econ 11:83–96

    MathSciNet  MATH  Google Scholar 

  • Saha M, Tripathi H, Dey S (2021) Single and double acceptance sampling plans for truncated life tests based on transmuted Rayleigh distribution. J Ind Prod Eng. https://doi.org/10.1080/21681015.2021.1893843

    Article  Google Scholar 

  • Shanker R, Shukla KK (2016) On modelling Of lifetime data using three-parameter generalized Lindley and generalized gamma distributions. Biom Biostat Int J 4(7)

  • Stephens KS (2001) The handbook of applied acceptance sampling: plans, procedures and principles. ASQ Quality Press, Milwaukee

    Google Scholar 

  • Tripathi H, Dey S, Saha M (2020a) Double and group acceptance sampling plan for truncated life test based on inverse log-logistic distribution. J Appl Stat 1–16

  • Tripathi H, Saha M, Alha V (2020b) An application of time truncated single acceptance sampling inspection plan based on generalized half-normal distribution. Ann Data Sci 1–13

  • Tripathi H, Maiti SS, Biswas S, Saha M (2020c) Sampling inspection plan for exponentially distributed quality characteristic and beyond. IAPQR Trans 44(2):157–173

    Article  Google Scholar 

  • Tripathi H, Al-Omari AI, Saha M, Alanzi ARA (2021) Improved attribute chain sampling plan for Darna distribution. Comput Syst Sci Eng 38(3):381–392

    Article  Google Scholar 

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Acknowledgements

The authors express their sincere thanks to the three esteemed Reviewers and the Editor for making some useful suggestions on an earlier version of this manuscript which resulted in this improved version.

Funding

This research received no external or internal funding.

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Authors and Affiliations

  1. Department of Statistics, Central University of Rajasthan, Ajmer, Rajasthan, India

    Mahendra Saha & Harsh Tripathi

  2. Department of Statistics, St. Anthony’s College, Shillong, Meghalaya, India

    Sanku Dey

  3. Department of Statistics, Visva-Bharati University, Santiniketan, India

    Sudhansu S. Maiti

Authors
  1. Mahendra Saha
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  2. Harsh Tripathi
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  3. Sanku Dey
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  4. Sudhansu S. Maiti
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All authors contributed equally to this work. All authors contributed equally to this work, have read and agreed to the published version of the manuscript.

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Correspondence to Mahendra Saha.

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Saha, M., Tripathi, H., Dey, S. et al. Acceptance sampling inspection plan for the Lindley and power Lindley distributed quality characteristics. Int J Syst Assur Eng Manag 12, 1410–1419 (2021). https://doi.org/10.1007/s13198-021-01349-8

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  • DOI: https://doi.org/10.1007/s13198-021-01349-8

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