A novel extension of Gumbel distribution: Statistical inference with Covid-19 application

Eslam Hossam; Alanazi Talal Abdulrahman; Ahmed M. Gemeay; Nawaf Alshammari; Etaf Alshawarbeh; Nour Khaled Mashaqbah

doi:10.1016/j.aej.2022.01.071

A novel extension of Gumbel distribution: Statistical inference with Covid-19 application

Eslam Hossam
, Alanazi Talal Abdulrahman
, Ahmed M. Gemeay
, Nawaf Alshammari
, Etaf Alshawarbeh
, Nour Khaled Mashaqbah

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

This paper introduced a relatively new statistical model as an extension of Gumbel distribution, which combines the new alpha power transformation method and Gumbel distribution. Different statistical properties of the proposed model were derived mathematically. Different estimation methods were introduced to estimate proposed model parameters. The behavior of these parameters was checked by using randomly generated data sets and the introduced estimation methods. Two real data sets were analyzed to show how the proposed model fits this data sets than its baseline model and many other well-known and related models.

Original language	English
Pages (from-to)	8823-8842
Number of pages	20
Journal	Alexandria Engineering Journal
Volume	61
Issue number	11
DOIs	https://doi.org/10.1016/j.aej.2022.01.071
State	Published - Nov 2022
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Covid 19
Estimation methods.
Gumbel distribution

Access to Document

10.1016/j.aej.2022.01.071

Cite this

@article{7387f3ac9bcd41e9a69ac57993745c20,

title = "A novel extension of Gumbel distribution: Statistical inference with Covid-19 application",

abstract = "This paper introduced a relatively new statistical model as an extension of Gumbel distribution, which combines the new alpha power transformation method and Gumbel distribution. Different statistical properties of the proposed model were derived mathematically. Different estimation methods were introduced to estimate proposed model parameters. The behavior of these parameters was checked by using randomly generated data sets and the introduced estimation methods. Two real data sets were analyzed to show how the proposed model fits this data sets than its baseline model and many other well-known and related models.",

keywords = "Covid 19, Estimation methods., Gumbel distribution",

author = "Eslam Hossam and Abdulrahman, \{Alanazi Talal\} and Gemeay, \{Ahmed M.\} and Nawaf Alshammari and Etaf Alshawarbeh and Mashaqbah, \{Nour Khaled\}",

note = "Publisher Copyright: {\textcopyright} 2022 THE AUTHORS",

year = "2022",

month = nov,

doi = "10.1016/j.aej.2022.01.071",

language = "English",

volume = "61",

pages = "8823--8842",

journal = "Alexandria Engineering Journal",

issn = "1110-0168",

publisher = "Elsevier B.V.",

number = "11",

}

TY - JOUR

T1 - A novel extension of Gumbel distribution

T2 - Statistical inference with Covid-19 application

AU - Hossam, Eslam

AU - Abdulrahman, Alanazi Talal

AU - Gemeay, Ahmed M.

AU - Alshammari, Nawaf

AU - Alshawarbeh, Etaf

AU - Mashaqbah, Nour Khaled

PY - 2022/11

Y1 - 2022/11

N2 - This paper introduced a relatively new statistical model as an extension of Gumbel distribution, which combines the new alpha power transformation method and Gumbel distribution. Different statistical properties of the proposed model were derived mathematically. Different estimation methods were introduced to estimate proposed model parameters. The behavior of these parameters was checked by using randomly generated data sets and the introduced estimation methods. Two real data sets were analyzed to show how the proposed model fits this data sets than its baseline model and many other well-known and related models.

AB - This paper introduced a relatively new statistical model as an extension of Gumbel distribution, which combines the new alpha power transformation method and Gumbel distribution. Different statistical properties of the proposed model were derived mathematically. Different estimation methods were introduced to estimate proposed model parameters. The behavior of these parameters was checked by using randomly generated data sets and the introduced estimation methods. Two real data sets were analyzed to show how the proposed model fits this data sets than its baseline model and many other well-known and related models.

KW - Covid 19

KW - Estimation methods.

KW - Gumbel distribution

UR - https://www.scopus.com/pages/publications/85125475753

U2 - 10.1016/j.aej.2022.01.071

DO - 10.1016/j.aej.2022.01.071

M3 - Article

AN - SCOPUS:85125475753

SN - 1110-0168

VL - 61

SP - 8823

EP - 8842

JO - Alexandria Engineering Journal

JF - Alexandria Engineering Journal

IS - 11

ER -

A novel extension of Gumbel distribution: Statistical inference with Covid-19 application

Abstract

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this