A Robust Framework for Probability Distribution Generation: Analyzing Structural Properties and Applications in Engineering and Medicine
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| Опубліковано в:: | Axioms vol. 14, no. 4 (2025), p. 281 |
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| Автор: | |
| Інші автори: | , , , , , |
| Опубліковано: |
MDPI AG
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| Онлайн доступ: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Mir Aadil Ahmad |u Department of Statistics, University of Kashmir, Srinagar 190006, India; aadilmir.stscholar@kashmiruniversity.net (A.A.M.); shamshad.stscholar@kashmiruniversity.net (S.U.R.); sprvz@uok.edu.in (S.P.A.) | |
| 245 | 1 | |a A Robust Framework for Probability Distribution Generation: Analyzing Structural Properties and Applications in Engineering and Medicine | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study introduces a novel trigonometric-based family of distributions for modeling continuous data through a newly proposed framework known as the ASP family, where ‘ASP’ represents the initials of the authors Aadil, Shamshad, and Parvaiz. A specific subclass of this family, termed the “ASP Rayleigh distribution” (ASPRD), is introduced that features two parameters. We conducted a comprehensive statistical analysis of the ASPRD, exploring its key properties and demonstrating its superior adaptability. The model parameters are estimated using four classical estimation methods: maximum likelihood estimation (MLE), least squares estimation (LSE), weighted least squares estimation (WLSE), and maximum product of spaces estimation (MPSE). Extensive simulation studies confirm these estimation techniques’ robustness, showing that biases, mean squared errors, and root mean squared errors consistently decrease as sample sizes increase. To further validate its applicability, we employ ASPRD on three real-world engineering datasets, showcasing its effectiveness in modeling complex data structures. This work not only strengthens the theoretical framework of probability distributions but also provides valuable tools for practical applications, paving the way for future advancements in statistical modeling. | |
| 653 | |a Maximum likelihood estimation | ||
| 653 | |a Errors | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Adaptability | ||
| 653 | |a Rayleigh distribution | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Statistical models | ||
| 653 | |a Probability distribution | ||
| 653 | |a Data structures | ||
| 653 | |a Flexibility | ||
| 700 | 1 | |a Rasool, Shamshad Ur |u Department of Statistics, University of Kashmir, Srinagar 190006, India; aadilmir.stscholar@kashmiruniversity.net (A.A.M.); shamshad.stscholar@kashmiruniversity.net (S.U.R.); sprvz@uok.edu.in (S.P.A.) | |
| 700 | 1 | |a Ahmad, S P |u Department of Statistics, University of Kashmir, Srinagar 190006, India; aadilmir.stscholar@kashmiruniversity.net (A.A.M.); shamshad.stscholar@kashmiruniversity.net (S.U.R.); sprvz@uok.edu.in (S.P.A.) | |
| 700 | 1 | |a Bhat, A A |u Department of Mathematical Sciences, Islamic University of Science and Technology, Pulwama 192122, India | |
| 700 | 1 | |a Jawa, Taghreed M |u Department of Mathematics and Statistics, College of Sciences, Taif University, Taif 21944, Saudi Arabia; tmjawa@tu.edu.sa (T.M.J.); neveen.s@tu.edu.sa (N.S.-A.) | |
| 700 | 1 | |a Sayed-Ahmed, Neveen |u Department of Mathematics and Statistics, College of Sciences, Taif University, Taif 21944, Saudi Arabia; tmjawa@tu.edu.sa (T.M.J.); neveen.s@tu.edu.sa (N.S.-A.) | |
| 700 | 1 | |a Tolba, Ahlam H |u Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt; dr_ahamdy156@mans.edu.eg | |
| 773 | 0 | |t Axioms |g vol. 14, no. 4 (2025), p. 281 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194489440/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194489440/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194489440/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |