Designing of Double Sampling Plan for Truncated
Life Tests Based on Percentiles using
Kumaraswamy Exponentiated Rayleigh
Distribution

S Jayalakshmi; Neena Krishna P K

doi:https://doi.org/10.14445/22315373/IJMTT-V68I1P503

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 68 | Issue 1 | Year 2022 | Article Id. IJMTT-V68I1P503 | DOI : https://doi.org/10.14445/22315373/IJMTT-V68I1P503

Designing of Double Sampling Plan for Truncated Life Tests Based on Percentiles using Kumaraswamy Exponentiated Rayleigh Distribution

S Jayalakshmi, Neena Krishna P K

Citation :

S Jayalakshmi, Neena Krishna P K, "Designing of Double Sampling Plan for Truncated Life Tests Based on Percentiles using Kumaraswamy Exponentiated Rayleigh Distribution," International Journal of Mathematics Trends and Technology (IJMTT), vol. 68, no. 1, pp. 29-35, 2022. Crossref, https://doi.org/10.14445/22315373/IJMTT-V68I1P503

Abstract

This Paper develops the Double Sampling Plan for life test using percentiles under the Kumaraswamy Exponentiated Rayleigh Distribution. A truncated life test may be conducted to evaluate the smallest sample size to insure certain percentile life time of products. The minimum sample size, specified ratio and operating characteristic values are calculated for various quality levels. The plan parameters and the measures are also studied for the proposed sampling plan which is suitable for the manufacturing industries for the selection of samples. Numerical illustrations and tables are also provided.

Keywords

Double Sampling Plan (DSP), Kumaraswamy Exponentiated Rayleigh distribution, Life Tests, Percentiles, Producer’s risk

References

[1] Baklizi, A.E.K. El Masri., Acceptance sampling plans based on truncated life tests in the Birnbaum Saunders model. Risk Analysis. 24 (2004) 453- 457.
[2] N. Balakrishnan, V. Leiva, J. Lopez., Acceptance sampling plans from truncated life tests based on the generalized Birnbaum-Saunders distribution. Communications in statistics – Simulation and Computation. 36 (2007) 643-656.
[3] Debasis Kundu, Mohammed, Z. Raqab., Generalized Rayleigh distribution: Different methods and estimations. Computational Statistics and Data Analysis, (2005).
[4] B. Epstein., Truncated life tests in the exponential case. Annals of Mathematical Statistics. 25 (1954) 555–564..
[5] S. S. Gupta, P. A. Groll., Gamma distribution in acceptance sampling based on life tests. Journal of the American Statistical Association. 56 (1961) 942–970.
[6] S. Jayalakshmi, P. K. Neena Krishna., Designing of Special Type Double Sampling Plan for Life Tests Based on Percentiles Using Exponentiated Frechet Distribution. Reliability Theory & Applications. 16(1) (2021) 117-123.
[7] R. R. L. Kantam, K. Rosaiah., Half Logistic distribution in acceptance sampling based on life tests. IAPQR Transactions .23(2) (1998) 117–125..
[8] Y. L. Lio, T. R. Tsai, S. J. Wu., Acceptance sampling plans from truncated life tests based on the Birnbaum-Saunders distribution for percentiles. Communications in Statistics -Simulation and Computation. 39(1) (2009) 119-136.
[9] Lord Rayleigh., On the resultant of a large number of vibrations of the same pitch and of arbitrary Phase. The London, Edinburg and Dublin philosophical magazine and Journal of Science. 10 (1880) 73-78.
[10] Nasr Ibrahim Rashwan., A Note on Kumaraswamy Exponentiated Rayleigh distribution,Journal of Statistical Theory and Applications. 15(3) (2016) 286-295.
[11] K. Pradeepaveerakumari, P. Ponneeswari., Designing of Acceptance Sampling Plan for life tests based on Percentiles of Exponentiated Rayleigh Distribution. International Journal of Current Engineering and Technology, 6(4) (2016) 1148-1153.
[12] G.S. Rao, R. R. L. Kantam, K. Rosaiah, J.P. Reddy., Acceptance sampling plans for percentiles based on the Inverse Rayleigh Distribution. Electronic Journal of Applied Statistical Analysis. 5(2) (2012) 164-177.
[13] T.R.Tsai, S. J. Wu., Acceptance sampling based on truncated life tests for generalized Rayleigh distribution. Journal of Applied Statistics, 33 (2006) 595-600.