Araştırma Makalesi
BibTex RIS Kaynak Göster

Kumaraswamy Inverse Lindley Distribution With Stress-Strength Reliability

Yıl 2020, Cilt: 6 Sayı: 3, 255 - 264, 27.12.2020

Öz

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.

Kaynakça

  • [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

Yıl 2020, Cilt: 6 Sayı: 3, 255 - 264, 27.12.2020

Öz

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.

Kaynakça

  • [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.
Toplam 2 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Metroloji,Uygulamalı ve Endüstriyel Fizik, Malzeme Üretim Teknolojileri
Bölüm Araştırma Makalesi
Yazarlar

Saeed E. Hemeda Bu kişi benim 0000-0002-5425-065X

Sukanta Pramanik 0000-0003-1621-7741

Sudhansu Maiti Bu kişi benim 0000-0001-8906-6513

Yayımlanma Tarihi 27 Aralık 2020
Gönderilme Tarihi 18 Mayıs 2020
Kabul Tarihi 13 Ekim 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 6 Sayı: 3

Kaynak Göster

IEEE S. E. Hemeda, S. Pramanik, ve S. Maiti, “Kumaraswamy Inverse Lindley Distribution With Stress-Strength Reliability”, GMBD, c. 6, sy. 3, ss. 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