Inverse power XLindley distribution with statistical inference and applications to engineering data
Abstract In this article, a new two-parameter distribution is constructed using the inverse transformation technique on the power XLindley distribution. It is called the inverse power XLindley distribution. As an attractive property, it can generate symmetric and asymmetric probability density funct...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87023-6 |
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| author | Amal S. Hassan Najwan Alsadat Christophe Chesneau Mohammed Elgarhy Mohamed Kayid Suleman Nasiru Ahmed M. Gemeay |
| author_facet | Amal S. Hassan Najwan Alsadat Christophe Chesneau Mohammed Elgarhy Mohamed Kayid Suleman Nasiru Ahmed M. Gemeay |
| author_sort | Amal S. Hassan |
| collection | DOAJ |
| description | Abstract In this article, a new two-parameter distribution is constructed using the inverse transformation technique on the power XLindley distribution. It is called the inverse power XLindley distribution. As an attractive property, it can generate symmetric and asymmetric probability density functions, ideal for modeling lifetime phenomena. In addition, it is suitable for various real data since the corresponding hazard rate function has an increasing, decreasing, reverse J-shape or J-shape. Essential characteristics and features of our study include quantiles, moments, inverse moments, probability-weighted moments, incomplete moments, and inequality measures. The inferences from the distribution are explored. In particular, the parameters are determined using twelve efficient estimation methods. These methods are maximum likelihood, Anderson-Darling, right-tailed Anderson-Darling, left-tailed Anderson-Darling, Cramér-von Mises, least squares, weighted least squares, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, percentiles, and Kolmogorov. The performance of the resulting estimates is analyzed using Monte Carlo simulation. The numerical results and graphical presentation indicate that the maximum product of spacing estimation approach has the highest accuracy and precision. Using three real data sets and comparisons with other distributions, the effectiveness of the proposed distribution is demonstrated and visually presented. |
| format | Article |
| id | doaj-art-ee737df8170a47a88d12483448db564a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ee737df8170a47a88d12483448db564a2025-02-09T12:33:26ZengNature PortfolioScientific Reports2045-23222025-02-0115113310.1038/s41598-025-87023-6Inverse power XLindley distribution with statistical inference and applications to engineering dataAmal S. Hassan0Najwan Alsadat1Christophe Chesneau2Mohammed Elgarhy3Mohamed Kayid4Suleman Nasiru5Ahmed M. Gemeay6Faculty of Graduate Studies for Statistical Research, Cairo UniversityDepartment of Quantitative Analysis, College of Business Administration, King Saud UniversityDepartment of Mathematics, Université de Caen NormandieDepartment of Basic Sciences, Higher Institute of Administrative SciencesDepartment of Statistics and Operations Research, College of Science, King Saud UniversityDepartment of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied SciencesDepartment of Mathematics, Faculty of Science, Tanta UniversityAbstract In this article, a new two-parameter distribution is constructed using the inverse transformation technique on the power XLindley distribution. It is called the inverse power XLindley distribution. As an attractive property, it can generate symmetric and asymmetric probability density functions, ideal for modeling lifetime phenomena. In addition, it is suitable for various real data since the corresponding hazard rate function has an increasing, decreasing, reverse J-shape or J-shape. Essential characteristics and features of our study include quantiles, moments, inverse moments, probability-weighted moments, incomplete moments, and inequality measures. The inferences from the distribution are explored. In particular, the parameters are determined using twelve efficient estimation methods. These methods are maximum likelihood, Anderson-Darling, right-tailed Anderson-Darling, left-tailed Anderson-Darling, Cramér-von Mises, least squares, weighted least squares, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, percentiles, and Kolmogorov. The performance of the resulting estimates is analyzed using Monte Carlo simulation. The numerical results and graphical presentation indicate that the maximum product of spacing estimation approach has the highest accuracy and precision. Using three real data sets and comparisons with other distributions, the effectiveness of the proposed distribution is demonstrated and visually presented.https://doi.org/10.1038/s41598-025-87023-6Power XLindley distributionInverse momentsEstimationMonte Carlo simulation |
| spellingShingle | Amal S. Hassan Najwan Alsadat Christophe Chesneau Mohammed Elgarhy Mohamed Kayid Suleman Nasiru Ahmed M. Gemeay Inverse power XLindley distribution with statistical inference and applications to engineering data Scientific Reports Power XLindley distribution Inverse moments Estimation Monte Carlo simulation |
| title | Inverse power XLindley distribution with statistical inference and applications to engineering data |
| title_full | Inverse power XLindley distribution with statistical inference and applications to engineering data |
| title_fullStr | Inverse power XLindley distribution with statistical inference and applications to engineering data |
| title_full_unstemmed | Inverse power XLindley distribution with statistical inference and applications to engineering data |
| title_short | Inverse power XLindley distribution with statistical inference and applications to engineering data |
| title_sort | inverse power xlindley distribution with statistical inference and applications to engineering data |
| topic | Power XLindley distribution Inverse moments Estimation Monte Carlo simulation |
| url | https://doi.org/10.1038/s41598-025-87023-6 |
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