On New Weibull Inverse Lomax Distribution with Applications

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Published: Jun 1, 2022
DOI: https://doi.org/10.21123/bsj.2022.19.3.0528

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Jamilu Yunusa Falgore
Department of Statistics, Ahmadu Bello University Zaria-Nigeria.
https://orcid.org/0000-0003-4762-1560
Sani Ibrahim Doguwa
Department of Statistics, Ahmadu Bello University Zaria-Nigeria.

Abstract

In this paper, simulation studies and applications of the New Weibull-Inverse Lomax (NWIL) distribution were presented. In the simulation studies, different sample sizes ranging from 30, 50, 100, 200, 300, to 500 were considered. Also, 1,000 replications were considered for the experiment. NWIL is a fat tail distribution. Higher moments are not easily derived except with some approximations. However, the estimates have higher precisions with low variances. Finally, the usefulness of the NWIL distribution was illustrated by fitting two data  sets

Received 18/12/2020,  Accepted 10/1/2021,  Published Online First 20/11/2021  

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1.
On New Weibull Inverse Lomax Distribution with Applications. Baghdad Sci.J [Internet]. 2022 Jun. 1 [cited 2024 Dec. 19];19(3):0528. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4615
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Vol. 19 No. 3 (2022): Issue 3
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article
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biography

Jamilu Yunusa Falgore, Department of Statistics, Ahmadu Bello University Zaria-Nigeria.

Department of Statistics, Ahmadu Bello University Zaria-Nigeria.

How to Cite

1.
On New Weibull Inverse Lomax Distribution with Applications. Baghdad Sci.J [Internet]. 2022 Jun. 1 [cited 2024 Dec. 19];19(3):0528. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4615
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References

Kleiber C, Kotz S. Statistical size distributions in economics and actuarial sciences. John Wiley & Sons, Inc., Hoboken, New Jersey. Wiley Interscience; 2003:188-189.

Kleiber C. Lorenz ordering of order statistics from log-logistic and related distributions. JSPI. 2004;120(1-2):13-19.

Rahman J, Aslam M, Ali S. Estimation and prediction of inverse Lomax model via a Bayesian approach. CJASR. 2013;2(3):43-56.

Rahman J, Aslam M. Interval prediction of future order statistics in the two-component mixture inverse Lomax model: a bayesian approach. AJMMS. 2014;33(3):216-227.

Jan U, Ahmad SP. Bayesian analysis of inverse Lomax distribution using approximation techniques.MTM.2017;7(7):1-12.

Rahman J, Aslam M. On the estimation of two-component mixture inverse Lomax model via a Bayesian approach. IJSAEM. 2017;8(1):99-109.

Yadav AS, Singh SK, Singh U. Bayesian estimation for inverse Lomax distribution under the progressive type-II censoring scheme. IJSAEM. 2019;10(5):905-917.

Falgore JY, Aliyu Y, Umar AA, Abdullahi UK. Odd generalized exponential-inverse Lomax distribution: properties and application. JNAMP.2018; 47:147–156.

Tahir MH, Cordeiro GM, Alizadeh M, Mansoor M, Zubair M, Hamedani GG. The odd generalized exponential family of distributions with applications. JSDA. 2015;2(1):1.

Maxwell O, Chukwu AU, Oyamakin OS, Khaleel MA. The Marshall-Olkin Inverse Lomax Distribution (MO-ILD) with Application on Cancer Stem Cell. JAMCS. 2019:1-12.

Marshall AW, Olkin I. A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika. 1997;84(3):641-652.

Hassan AS, Mohamed RE. Weibull Inverse Lomax Distribution. PJSOR. 2019;15(3):587-603.

Falgore JY, Doguwa SI, Isah A. The Weibull-Inverse Lomax (WIL) distribution with Application on Bladder Cancer. BBIJ. 2019a;8(6):195‒202. DOI: 10.15406/bbij.2019.08.00289.

Bourguignon M, Silva RB, Cordeiro GM. The Weibull-G family of probability distributions. JDS. 2014;12(1):53-68.

Falgore JY, Doduwa SI, Isah A. On the Properties of New Weibull-Inverse Lomax Distribution. TNAMP.2019b;(1):89-96.

Tahir MH, Zubair M, Mansoor M, Cordeiro GM, Alizadehk M, Hamedani GG. A new Weibull-G family of distributions. HJMS.2016;45(2):629-647.

Yadav AS, Singh SK, Singh U. On hybrid censored inverse Lomax distribution: Application to the survival data. Statistica. 2016;76(2):185-203.

Sambridge M, Mosegaard K. Monte Carlo methods in geophysical inverse problems. RG. 2002;40(3):1009.DOI:10.1029/2000RG000089.

Mooney CZ. Monte Carlo simulation. Sage Publications; 1997.

Walther BA, Moore JL. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography. 2005;28(6):815-829.

Duncan AJ. Quality Control and Industrial Statistics, fourth edition (Irwin- Homewood, 1974).

Henningsen A, Toomet O. maxLik: A package for maximum likelihood estimation in R. CS. 2011;26(3):443-458.

Team RC. R: A Language and Environment for Statistical Computing http://www. R-project. org. 2014.

Zubair M, Cordeiro GM, Tahir MH, Mahmood M, Mansoor M. A Study of Logistic-Lomax Distribution and Its Applications. JPSS. 2017:29.

Oguntunde PE, Balogun OS, Okagbue HI, Bishop SA. The Weibull-exponential distribution: Its properties and applications. JAS. 2015;15(11):1305-1311.

Bjerkedal T. Acquisition of Resistance in Guinea Pies infected with Different Doses of Virulent Tubercle Bacilli. AJH. 1960;72(1):130-48.