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...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-12-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/7/1/3 |
id |
doaj-b92e596b191547ddbdeb49e633b2f743 |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT ibrahimelbatal themodifiedbetagompertzdistributiontheoryandapplications AT farrukhjamal themodifiedbetagompertzdistributiontheoryandapplications AT christophechesneau themodifiedbetagompertzdistributiontheoryandapplications AT mohammedelgarhy themodifiedbetagompertzdistributiontheoryandapplications AT sharifahalrajhi themodifiedbetagompertzdistributiontheoryandapplications AT ibrahimelbatal modifiedbetagompertzdistributiontheoryandapplications AT farrukhjamal modifiedbetagompertzdistributiontheoryandapplications AT christophechesneau modifiedbetagompertzdistributiontheoryandapplications AT mohammedelgarhy modifiedbetagompertzdistributiontheoryandapplications AT sharifahalrajhi modifiedbetagompertzdistributiontheoryandapplications |
_version_ |
1725189635231973376 |