A new unit distribution : properties, estimation, and regression analysis
© 2024. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
|---|---|
| Enthalten in: |
Scientific reports - 14(2024), 1 vom: 27. März, Seite 7214 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Karakaya, Kadir [VerfasserIn] |
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| Links: |
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| Themen: |
Beta regression model |
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| Anmerkungen: |
Date Revised 30.03.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1038/s41598-024-57390-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370215591 |
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| 245 | 1 | 2 | |a A new unit distribution |b properties, estimation, and regression analysis |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Beta regression model | |
| 650 | 4 | |a Educational attainment dataset | |
| 650 | 4 | |a Monte Carlo simulation | |
| 650 | 4 | |a Quantile regression analysis | |
| 650 | 4 | |a Stochastic ordering | |
| 700 | 1 | |a Rajitha, C S |e verfasserin |4 aut | |
| 700 | 1 | |a Sağlam, Şule |e verfasserin |4 aut | |
| 700 | 1 | |a Tashkandy, Yusra A |e verfasserin |4 aut | |
| 700 | 1 | |a Bakr, M E |e verfasserin |4 aut | |
| 700 | 1 | |a Muse, Abdisalam Hassan |e verfasserin |4 aut | |
| 700 | 1 | |a Kumar, Anoop |e verfasserin |4 aut | |
| 700 | 1 | |a Hussam, Eslam |e verfasserin |4 aut | |
| 700 | 1 | |a Gemeay, Ahmed M |e verfasserin |4 aut | |
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