The Modified Beta Gompertz Distribution: Theory and Applications

In this paper, we introduce a new continuous probability distribution with five parameters called the modified beta Gompertz distribution. It is derived from the modified beta generator proposed by Nadarajah, Teimouri and Shih (2014) and the Gompertz distribution. By investigating its mathematical a...

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Main Authors: Ibrahim Elbatal, Farrukh Jamal, Christophe Chesneau, Mohammed Elgarhy, Sharifah Alrajhi
Format: Article
Language:English
Published: MDPI AG 2018-12-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/7/1/3
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spelling doaj-b92e596b191547ddbdeb49e633b2f7432020-11-25T01:06:33ZengMDPI AGMathematics2227-73902018-12-0171310.3390/math7010003math7010003The Modified Beta Gompertz Distribution: Theory and ApplicationsIbrahim Elbatal0Farrukh Jamal1Christophe Chesneau2Mohammed Elgarhy3Sharifah Alrajhi4Department of Mathematics and Statistics, College of Science Al Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 65892, Riyadh 11566, Saudi ArabiaDepartment of Statistics, Govt. S.A Postgraduate College Dera Nawab Sahib, Bahawalpur, Punjab 63360, PakistanDepartment of Mathematics, LMNO, University of Caen, 14032 Caen, FranceDepartment of Statistics, University of Jeddah, Jeddah 21589, Saudi ArabiaDepartment of Statistics, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi ArabiaIn this paper, we introduce a new continuous probability distribution with five parameters called the modified beta Gompertz distribution. It is derived from the modified beta generator proposed by Nadarajah, Teimouri and Shih (2014) and the Gompertz distribution. By investigating its mathematical and practical aspects, we prove that it is quite flexible and can be used effectively in modeling a wide variety of real phenomena. Among others, we provide useful expansions of crucial functions, quantile function, moments, incomplete moments, moment generating function, entropies and order statistics. We explore the estimation of the model parameters by the obtained maximum likelihood method. We also present a simulation study testing the validity of maximum likelihood estimators. Finally, we illustrate the flexibility of the distribution by the consideration of two real datasets.https://www.mdpi.com/2227-7390/7/1/3modified beta generatorgompertz distributionmaximum likelihood estimation
collection DOAJ
language English
format Article
sources DOAJ
author Ibrahim Elbatal
Farrukh Jamal
Christophe Chesneau
Mohammed Elgarhy
Sharifah Alrajhi
spellingShingle Ibrahim Elbatal
Farrukh Jamal
Christophe Chesneau
Mohammed Elgarhy
Sharifah Alrajhi
The Modified Beta Gompertz Distribution: Theory and Applications
Mathematics
modified beta generator
gompertz distribution
maximum likelihood estimation
author_facet Ibrahim Elbatal
Farrukh Jamal
Christophe Chesneau
Mohammed Elgarhy
Sharifah Alrajhi
author_sort Ibrahim Elbatal
title The Modified Beta Gompertz Distribution: Theory and Applications
title_short The Modified Beta Gompertz Distribution: Theory and Applications
title_full The Modified Beta Gompertz Distribution: Theory and Applications
title_fullStr The Modified Beta Gompertz Distribution: Theory and Applications
title_full_unstemmed The Modified Beta Gompertz Distribution: Theory and Applications
title_sort modified beta gompertz distribution: theory and applications
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2018-12-01
description In this paper, we introduce a new continuous probability distribution with five parameters called the modified beta Gompertz distribution. It is derived from the modified beta generator proposed by Nadarajah, Teimouri and Shih (2014) and the Gompertz distribution. By investigating its mathematical and practical aspects, we prove that it is quite flexible and can be used effectively in modeling a wide variety of real phenomena. Among others, we provide useful expansions of crucial functions, quantile function, moments, incomplete moments, moment generating function, entropies and order statistics. We explore the estimation of the model parameters by the obtained maximum likelihood method. We also present a simulation study testing the validity of maximum likelihood estimators. Finally, we illustrate the flexibility of the distribution by the consideration of two real datasets.
topic modified beta generator
gompertz distribution
maximum likelihood estimation
url https://www.mdpi.com/2227-7390/7/1/3
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