A novel quantile regression for fractiles based on unit logistic exponential distribution
Continuous developments in unit interval distributions have shown effectiveness in modeling proportional data. However, challenges persist in diverse dispersion characteristics in real-world scenarios. This study introduces the unit logistic-exponential (ULE) distribution, a flexible probability mod...
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AIMS Press
2024-12-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241644 |
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| author | Hanan Haj Ahmad Kariema A. Elnagar |
| author_facet | Hanan Haj Ahmad Kariema A. Elnagar |
| author_sort | Hanan Haj Ahmad |
| collection | DOAJ |
| description | Continuous developments in unit interval distributions have shown effectiveness in modeling proportional data. However, challenges persist in diverse dispersion characteristics in real-world scenarios. This study introduces the unit logistic-exponential (ULE) distribution, a flexible probability model built upon the logistic-exponential distribution and designed for data confined to the unit interval. The statistical properties of the ULE distribution were studied, and parameter estimation through maximum likelihood estimation, Bayesian methods, maximum product spacings, and least squares estimates were conducted. A thorough simulation analysis using numerical techniques such as the quasi-Newton method and Markov chain Monte Carlo highlights the performance of the estimation methods, emphasizing their accuracy and reliability. The study reveals that the ULE distribution, paired with tools like randomized quantile and Cox-Snell residuals, provides robust assessments of goodness of fit, making it well-suited for real-world applications. Key findings demonstrate that the unit logistic-exponential distribution captures diverse data patterns effectively and improves reliability assessment in practical contexts. When applied to two real-world datasets—one from the medical field and the other from the economic sector—the ULE distribution consistently outperforms existing unit interval models, showcasing lower error rates and enhanced flexibility in tail behavior. These results underline the distribution's potential impact in areas requiring precise proportions modeling, ultimately supporting better decision-making and predictive analyses. |
| format | Article |
| id | doaj-art-95171842a1b745bdb6d3025d365ca84e |
| institution | Kabale University |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-95171842a1b745bdb6d3025d365ca84e2025-01-23T07:53:25ZengAIMS PressAIMS Mathematics2473-69882024-12-01912345043453610.3934/math.20241644A novel quantile regression for fractiles based on unit logistic exponential distributionHanan Haj Ahmad0Kariema A. Elnagar1Department of Basic Science, The General Administration of Preparatory Year, King Faisal University, Hofuf 31982, Al-Ahsa, Saudi ArabiaBasic Sciences Department, Misr Higher Institute of Commerce and Computers, Mansoura, EgyptContinuous developments in unit interval distributions have shown effectiveness in modeling proportional data. However, challenges persist in diverse dispersion characteristics in real-world scenarios. This study introduces the unit logistic-exponential (ULE) distribution, a flexible probability model built upon the logistic-exponential distribution and designed for data confined to the unit interval. The statistical properties of the ULE distribution were studied, and parameter estimation through maximum likelihood estimation, Bayesian methods, maximum product spacings, and least squares estimates were conducted. A thorough simulation analysis using numerical techniques such as the quasi-Newton method and Markov chain Monte Carlo highlights the performance of the estimation methods, emphasizing their accuracy and reliability. The study reveals that the ULE distribution, paired with tools like randomized quantile and Cox-Snell residuals, provides robust assessments of goodness of fit, making it well-suited for real-world applications. Key findings demonstrate that the unit logistic-exponential distribution captures diverse data patterns effectively and improves reliability assessment in practical contexts. When applied to two real-world datasets—one from the medical field and the other from the economic sector—the ULE distribution consistently outperforms existing unit interval models, showcasing lower error rates and enhanced flexibility in tail behavior. These results underline the distribution's potential impact in areas requiring precise proportions modeling, ultimately supporting better decision-making and predictive analyses.https://www.aimspress.com/article/doi/10.3934/math.20241644logistic exponential distributionquantile regression modelmaximum likelihood estimationbayes estimationmarkov chain monte carlorandomized quantile residualcox-snell residual |
| spellingShingle | Hanan Haj Ahmad Kariema A. Elnagar A novel quantile regression for fractiles based on unit logistic exponential distribution AIMS Mathematics logistic exponential distribution quantile regression model maximum likelihood estimation bayes estimation markov chain monte carlo randomized quantile residual cox-snell residual |
| title | A novel quantile regression for fractiles based on unit logistic exponential distribution |
| title_full | A novel quantile regression for fractiles based on unit logistic exponential distribution |
| title_fullStr | A novel quantile regression for fractiles based on unit logistic exponential distribution |
| title_full_unstemmed | A novel quantile regression for fractiles based on unit logistic exponential distribution |
| title_short | A novel quantile regression for fractiles based on unit logistic exponential distribution |
| title_sort | novel quantile regression for fractiles based on unit logistic exponential distribution |
| topic | logistic exponential distribution quantile regression model maximum likelihood estimation bayes estimation markov chain monte carlo randomized quantile residual cox-snell residual |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241644 |
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