Unit extended exponential distribution with applications
In this paper, the unit extended exponential distribution is presented. The extended exponential distribution is a two-parameter lifespan distribution that has been demonstrated to be very adaptable. Our goal is to convert this flexibility between the unit interval and the extended exponential distr...
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| Published in | Journal of radiation research and applied sciences Vol. 17; no. 4; p. 101118 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.12.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-8507 1687-8507 |
| DOI | 10.1016/j.jrras.2024.101118 |
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| Abstract | In this paper, the unit extended exponential distribution is presented. The extended exponential distribution is a two-parameter lifespan distribution that has been demonstrated to be very adaptable. Our goal is to convert this flexibility between the unit interval and the extended exponential distribution. Several different types of probability density functions, including decreasing, left-skewed, right-skewed, and unimodal, have been shown. However, the hazard rate function may take on various forms, including J-shaped, U-shaped, and bathtub shapes. A few statistical properties, such as moments, mean, variance, skewness, kurtosis, expectation-weighted moments, incomplete moments, conditional moments, mean deviation, Lorenz and Bonferroni curves, mean residual life, mean inactivity time, and various entropy measures, round out the theoretical section. The maximum likelihood technique is utilized to estimate model parameters using unit data. This study provides simulation data to evaluate the proposed strategy. Two applications utilizing actual data sets emphasize the significance of the new model compared to existing unit models. |
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| AbstractList | In this paper, the unit extended exponential distribution is presented. The extended exponential distribution is a two-parameter lifespan distribution that has been demonstrated to be very adaptable. Our goal is to convert this flexibility between the unit interval and the extended exponential distribution. Several different types of probability density functions, including decreasing, left-skewed, right-skewed, and unimodal, have been shown. However, the hazard rate function may take on various forms, including J-shaped, U-shaped, and bathtub shapes. A few statistical properties, such as moments, mean, variance, skewness, kurtosis, expectation-weighted moments, incomplete moments, conditional moments, mean deviation, Lorenz and Bonferroni curves, mean residual life, mean inactivity time, and various entropy measures, round out the theoretical section. The maximum likelihood technique is utilized to estimate model parameters using unit data. This study provides simulation data to evaluate the proposed strategy. Two applications utilizing actual data sets emphasize the significance of the new model compared to existing unit models. |
| ArticleNumber | 101118 |
| Author | Alsadat, Najwan Ragab, Ibrahim E. Elgarhy, Mohammed Balogun, Oluwafemi Samson |
| Author_xml | – sequence: 1 givenname: Ibrahim E. surname: Ragab fullname: Ragab, Ibrahim E. email: ibrahim.ragab@eia.edu.eg organization: Department of Basic Sciences, Egyptian Institute of Alexandria Academy for Management and Accounting, EIA, Alexandria, Egypt – sequence: 2 givenname: Najwan surname: Alsadat fullname: Alsadat, Najwan email: nalsadat@ksu.edu.sa organization: Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587, Saudi Arabia – sequence: 3 givenname: Oluwafemi Samson surname: Balogun fullname: Balogun, Oluwafemi Samson email: samsb@student.uef.fi organization: Department of Computing, University of Eastern Finland, FI-70211, Finland – sequence: 4 givenname: Mohammed orcidid: 0000-0002-1333-3862 surname: Elgarhy fullname: Elgarhy, Mohammed email: dr.moelgarhy@gmail.com organization: Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt |
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| Cites_doi | 10.1007/s40314-021-01418-5 10.35378/gujs.776277 10.17713/ajs.v52i2.1407 10.1080/00949658008810388 10.1016/0142-0615(83)90011-X 10.1080/10543406.2023.2210681 10.1111/1467-842X.00072 10.1016/0022-1694(80)90036-0 10.1080/02664763.2018.1511774 10.1016/S0019-9958(71)90065-9 10.1080/01966324.2020.1834893 10.1080/09720510.2019.1646503 10.1111/j.1467-842X.1977.tb01277.x 10.3390/e21070696 10.1080/03610926.2018.1476717 10.1007/s40300-013-0007-y 10.1007/BF02602999 10.3390/math11041007 10.1080/02331881003678678 10.15446/rce.v37n1.44355 10.1002/j.1538-7305.1948.tb01338.x 10.1080/16843703.2022.2049508 10.1111/j.2517-6161.1958.tb00278.x 10.1016/j.aej.2024.02.063 |
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| Keywords | Entropy Unit distributions Monte-Carlo simulation Extended exponential distribution Real data applications |
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| Title | Unit extended exponential distribution with applications |
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