American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
Open Access
Journal Browser
American Journal of Applied Mathematics and Statistics. 2014, 2(6), 364-368
DOI: 10.12691/ajams-2-6-2
Open AccessArticle

The Exponentiated Lomax Distribution: Different Estimation Methods

Hamdy M. Salem1,

1Department of Statistics, Faculty of Commerce, Al-Azher University, Egypt & Community College in Buraidah, Qassim University, Saudi Arabia

Pub. Date: November 11, 2014

Cite this paper:
Hamdy M. Salem. The Exponentiated Lomax Distribution: Different Estimation Methods. American Journal of Applied Mathematics and Statistics. 2014; 2(6):364-368. doi: 10.12691/ajams-2-6-2


This paper concerns with the estimation of parameters for the Exponentiated Lomax Distribution ELD. Different estimation methods such as maximum likelihood, quasi-likelihood, Bayesian and quasi-Bayesian are used to evaluate parameters. Numerical study is discussed to illustrate the optimal procedure using MATHCAD program (2001). A comparison between the four estimation methods will be performed.

Exponentiated Lomax Distribution maximum likelihood estimation quasi-likelihood estimation bayesian estimation quasi-bayesian estimation

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


[1]  Alzaatreh A., Lee C. & Felix F. (2013). “A new method for generating families of continuous distributions”. METRON. 71. 63-79.
[2]  Amos D. (1983) “A portable Fortran subroutine for derivatives of Psi function”. ACM Transactions on Mathematical software 9 (4). 494-502.
[3]  Cordeiro M. & Castro M. (2011). “A new family of generalized distributions”. Journal of Statistical. Computation and Simulation. 81 (7). 883-898.
[4]  Gauss M. & Cordeiro M (2013). “The Exponentiated Generalized Class of Distributions”. Journal of Data Science. 11. 1-12.
[5]  Gupta C., Gupta P., & Gupta D. (1998). “Modeling failure time data by Lehmann alternatives”. Communications in Statistics-Theory and Methods. 27. 887-904.
[6]  Nadarajah S. & Kotz S. (2006). “The Exponentiated Type Distributions”. Acta Applicandae Mathematica. 92 (2). 97-111.
[7]  Salem H. (2013). “Inference on Stress-Strength Reliability for Weighted Weibull Distribution”. American Journal of Mathematics and Statistics. 3 (4): 220-226.
[8]  Wedderburn, R. (1974) “Quasi-Likelihood Functions, Generalized Models and the Gauss-Newton Method”. Biometrika. 61 (3). 439-443.