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 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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Publication Details

Journal
Journal of Radiation Research and Applied Sciences
Published
2024-09-26
DOI
https://doi.org/10.1016/j.jrras.2024.101118
Citations
15
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
10.64

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article

Unit extended exponential distribution with applications

Oluwafemi Samson Balogun, Ibrahim E. Ragab, Mohammed Elgarhy, Najwan Alsadat
15 citations
Journal of Radiation Research and Applied Sciences
Statistical Distribution Estimation and Applications
10.64
article

Unit extended exponential distribution with applications

Oluwafemi Samson Balogun, Ibrahim E. Ragab, Mohammed Elgarhy, Najwan Alsadat
article en
15 citations

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.

Journal of Radiation Research and Applied SciencesVol. 17(4)
Beni-Suef University (EG), University of Eastern Finland (FI), King Saud University (SA), Finland University (FI), Egyptian Institute of Alexandria Academy for Administration & Accounting (EG), High Institute for Management Sciences (EG), University of Business and Technology (SA)
King Saud University, Deanship of Scientific Research, King Saud University
Openalex Percentile: Top 1%
Statistical Distribution Estimation and Applications
10.64
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Unit extended exponential distribution with applications — Oluwafemi Samson Balogun, Ibrahim E. Ragab, et al. · Journal of Radiation Research and Applied Sciences (2024) | TGRS Research Map | TGRS