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Kumaraswamy Inverse Lindley Distribution With Stress-Strength Reliability

Year 2020, Volume: 6 Issue: 3, 255 - 264, 27.12.2020

Abstract

A new generalizing of inverse Lindley distribution called Kumaraswamy inverse Lindley is presented in this study. Some mathematical expressions are determined to the proposed distribution. Significant statistical measures are deduced including quantiles generating functions, ordinary and incomplete moments, entropies, mean deviations and order statistics. Some different properties such as, median, mean, variance, coefficient of variation, coefficients of skewness and kurtosis are characterized. Moreover, stress-strength reliabilities defined. Simulation study of the Kumaraswamy inverse distribution is introducedusing maximum likelihood estimation and the performances of their estimates are compered through biases and mean square errors. The applicability and importance of the new distribution is conducted through two real data groups.

References

  • [1] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
  • [2] Al-Babtain, A., Fattah, A. A., Ahmed, A. H. N., and Merovci, F. (2017). The Kumaraswamy-transmuted exponentiated modified Weibull distribution. Communications in Statistics-Simulation and Computation, 46(5), 3812-3832.

Kumaraswamy Inverse Lindley Distribution with Stress-Strength Reliability

Year 2020, Volume: 6 Issue: 3, 255 - 264, 27.12.2020

Abstract

A new generalizing of inverse Lindley distribution called Kumaraswamy inverse Lindley is presented in this study. Some mathematical expressions are determined to the proposed distribution. Significant statistical measures are deduced including quantiles generating functions, ordinary and incomplete moments, entropies, mean deviations and order statistics. Some different properties such as, median, mean, variance, coefficient of variation, coefficients of skewness and kurtosis are characterized. Moreover, stress-strength reliabilities defined. Simulation study of the Kumaraswamy inverse distribution is introduced using maximum likelihood estimation and the performances of their estimates are compered through biases and mean square errors. The applicability and importance of the new distribution is conducted through two real data groups.

References

  • [1] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
  • [2] Al-Babtain, A., Fattah, A. A., Ahmed, A. H. N., and Merovci, F. (2017). The Kumaraswamy-transmuted exponentiated modified Weibull distribution. Communications in Statistics-Simulation and Computation, 46(5), 3812-3832.
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Details

Primary Language English
Subjects Metrology, Applied and Industrial Physics, Material Production Technologies
Journal Section Research Articles
Authors

Saeed E. Hemeda This is me 0000-0002-5425-065X

Sukanta Pramanik 0000-0003-1621-7741

Sudhansu Maiti This is me 0000-0001-8906-6513

Publication Date December 27, 2020
Submission Date May 18, 2020
Acceptance Date October 13, 2020
Published in Issue Year 2020 Volume: 6 Issue: 3

Cite

IEEE S. E. Hemeda, S. Pramanik, and S. Maiti, “Kumaraswamy Inverse Lindley Distribution With Stress-Strength Reliability”, GJES, vol. 6, no. 3, pp. 255–264, 2020.

Gazi Journal of Engineering Sciences (GJES) publishes open access articles under a Creative Commons Attribution 4.0 International License (CC BY). 1366_2000-copia-2.jpg