A new unit distribution: properties, estimation, and regression analysis
Abstract This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-57390-7 |
| _version_ | 1797233676170297344 |
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| author | Kadir Karakaya C. S. Rajitha Şule Sağlam Yusra A. Tashkandy M. E. Bakr Abdisalam Hassan Muse Anoop Kumar Eslam Hussam Ahmed M. Gemeay |
| author_facet | Kadir Karakaya C. S. Rajitha Şule Sağlam Yusra A. Tashkandy M. E. Bakr Abdisalam Hassan Muse Anoop Kumar Eslam Hussam Ahmed M. Gemeay |
| author_sort | Kadir Karakaya |
| collection | DOAJ |
| description | Abstract This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson–Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields. |
| first_indexed | 2024-04-24T16:19:57Z |
| format | Article |
| id | doaj.art-943de7a55b8c4117b1591ec687e331d0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T16:19:57Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-943de7a55b8c4117b1591ec687e331d02024-03-31T11:15:23ZengNature PortfolioScientific Reports2045-23222024-03-0114111610.1038/s41598-024-57390-7A new unit distribution: properties, estimation, and regression analysisKadir Karakaya0C. S. Rajitha1Şule Sağlam2Yusra A. Tashkandy3M. E. Bakr4Abdisalam Hassan Muse5Anoop Kumar6Eslam Hussam7Ahmed M. Gemeay8Department of Statistics, Faculty of Sciences, Selcuk UniversityDepartment of Mathematics, Amrita School of Physical SciencesDepartment of Statistics, Faculty of Sciences, Selcuk UniversityDepartment of Statistics and Operations Research, College of Science, King Saud UniversityDepartment of Statistics and Operations Research, College of Science, King Saud UniversityFaculty of Science and Humanities, School of Postgraduate Studies and Research (SPGSR), Amoud UniversityDepartment of Statistics, Faculty of Basic Science, Central University of HaryanaDepartment of Mathematics, Faculty of Science, Helwan UniversityDepartment of Mathematics, Faculty of Science, Tanta UniversityAbstract This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson–Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.https://doi.org/10.1038/s41598-024-57390-7Stochastic orderingMonte Carlo simulationQuantile regression analysisBeta regression modelEducational attainment dataset |
| spellingShingle | Kadir Karakaya C. S. Rajitha Şule Sağlam Yusra A. Tashkandy M. E. Bakr Abdisalam Hassan Muse Anoop Kumar Eslam Hussam Ahmed M. Gemeay A new unit distribution: properties, estimation, and regression analysis Scientific Reports Stochastic ordering Monte Carlo simulation Quantile regression analysis Beta regression model Educational attainment dataset |
| title | A new unit distribution: properties, estimation, and regression analysis |
| title_full | A new unit distribution: properties, estimation, and regression analysis |
| title_fullStr | A new unit distribution: properties, estimation, and regression analysis |
| title_full_unstemmed | A new unit distribution: properties, estimation, and regression analysis |
| title_short | A new unit distribution: properties, estimation, and regression analysis |
| title_sort | new unit distribution properties estimation and regression analysis |
| topic | Stochastic ordering Monte Carlo simulation Quantile regression analysis Beta regression model Educational attainment dataset |
| url | https://doi.org/10.1038/s41598-024-57390-7 |
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