CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Kumaraswamy Inverse Flexible Weibull Distribution: Theory and Application

by Jamal N. Al Abbasi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 7
Year of Publication: 2016
Authors: Jamal N. Al Abbasi
10.5120/ijca2016912223

Jamal N. Al Abbasi . Kumaraswamy Inverse Flexible Weibull Distribution: Theory and Application. International Journal of Computer Applications. 154, 7 ( Nov 2016), 41-46. DOI=10.5120/ijca2016912223

@article{ 10.5120/ijca2016912223,
author = { Jamal N. Al Abbasi },
title = { Kumaraswamy Inverse Flexible Weibull Distribution: Theory and Application },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 7 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number7/26507-2016912223/ },
doi = { 10.5120/ijca2016912223 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:39.079801+05:30
%A Jamal N. Al Abbasi
%T Kumaraswamy Inverse Flexible Weibull Distribution: Theory and Application
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 7
%P 41-46
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A generalization of the Inverse flexible weibull distribution so-called the Kumaraswamy-Inverse flexible weibull distribution is proposed and studied. Various structural properties including explicit expressions for the moments, quantiles and moment generating function of the new distribution are derived. The estimation of the model parameters is performed by maximum likelihood method and the observed Fisher’s information matrix is derived. For different values of sample sizes, Monte Carlo simulation is performed to investigate the precision of the maximum likelihood estimates. The usefulness of the kumaraswamy inverse flexible distribution for modeling data is illustrated using real data.

References
  1. El-Gohary, A., El-Bassiouny, A. H. and El-Morshedy M. (2015). Inverse Flexible Weibull Extension Distribution, International Journal of Computer Applications, 115(2), 46-51.
  2. Bebbington, M. S., Lai, C. D. and Zitikis, R. (2007). A flexible Weibull extension. Reliability Engineering & System Safety, 92(6), 719–26.
  3. Marshall A.W. and Olkin I. (1997). A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families, Biometrika, 84, 641-652.
  4. Eugene, N., et al. (2002). Beta-Normal distribution and its applications. Communications in Statistics - Theory and Methods, 31(4), 497–512.
  5. Shaw. W.T, Buckley I.R.C., (2007). The alchemy of probability distributions: beyond Gram- Charlier expansions, and a skew-kurtotic-normal distribution from a rank transmutation map. Technical report.
  6. Cordeiro G.M. and de Castro M. (2011). A new family of generalized distributions. Journal of Statistical Computation and Simulation, 81(7), 883-898.
  7. Alexander, C., Cordeiro, G.M., Ortega, E.M.M. and Sarabia, J.M. (2012). Generalized beta generated distributions, Computational Statistics and Data Analysis, 56, 1880–1897.
  8. Zografos, K. and Balakrishnan, N. (2009). On families of beta- and generalized gamma-generated distributions and associated inference. Statistical Methodology, 6, 344–362.
  9. Ristic M.M., Balakrishnan N. (2012). The Gamma-exponentiated Exponential Distribution. Journal of Statistical Computation and Simulation, 82(8), 1191-1206.
  10. Amini, M., Mir Mostafaee, S.M.T.K. and Ahmadi, J. (2012). Log-gamma-generated families of distributions, Statistics, 48, 913–932.
  11. Torabi, H. and Montazari, N.H. (2014). The logistic-uniform distribution and its application. Communications in Statistics–Simulation and Computation, 43, 2551–2569.
  12. Cordeiro, G.M., Ortega, E.M.M. and da Cunha, D.C.C. (2013). The exponentiated generalized class of distributions, Journal of Data Science, 11, 1–27.
  13. Alzaatreh, A., Lee, C. and Famoye, F. (2013). A new method for generating families of continuous distributions, Metron,
  14. Bourguignon, M., Silva, R.B. and Cordeiro, G.M. (2014). The Weibull–G family of probability distributions, Journal of Data Science, 12, 53–68.
  15. Tahir M.H., Cordeiro G.M., Alzaatreh A., Mansoor M., Zubair M. (2016).The logistic-X family of distributions and its applications, Commun. Stat. Theory Methods, (forthcoming).
  16. Jones, M.C. (2009). Kumaraswamy’s distribution: A beta-type distribution with some tractability advantages. Statistical Methodology, 6, 70-81.
  17. Cox, D.R. and Hinkley, D.V. (1974). Theoretical Statistics. Chapman & Hall, London.
  18. Murthy, D.N.P, Xie, M. and Jian, R. (2004). Weibull Models.Wiley.
Index Terms

Computer Science
Information Sciences

Keywords

Flexible Weibull Distribution Kumaraswamy-G Class Hazard Function Maximum Likelihood Reliability.

Powered by PhDFocusTM