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一种瑞利分布新型扩展的开发及其在COVID-19数据中的应用。

Development of a novel extension of Rayleigh distribution with application to COVID-19 data.

作者信息

Qayoom Danish, Rather Aafaq A, Alqasem Ohud A, Ahmad Zahoor, Nagy M, Yousuf Abdirashid M, Mansi A H, Hussam Eslam, Gemeay Ahmed M

机构信息

Symbiosis Statistical Institute, Symbiosis International (Deemed University), Pune-411004, India.

Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

出版信息

Sci Rep. 2025 May 27;15(1):18535. doi: 10.1038/s41598-025-03645-w.

DOI:10.1038/s41598-025-03645-w
PMID:40425741
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12117054/
Abstract

Effective analysis of medical data is essential for understanding complex healthcare phenomena. Probability distribution models offer a structured approach to uncover patterns in such data, particularly for studying disease progression, survival analysis and many more. In this study, we explore a novel probability distribution model, derived by applying the DUS transformation to the standard Rayleigh distribution. We thoroughly investigate the statistical properties of the proposed model and derive key reliability measures to demonstrate its applicability in reliability analysis. To ensure precise parameter estimation, various estimation methods are evaluated, and their effectiveness is assessed through a detailed simulation study using bias, mean squared error, and mean relative error as performance criteria. The developed model's practical applicability is demonstrated with an analysis of COVID-19 data, comparing its performance with several well-known distributions. The results highlight the flexibility and accuracy of the model, establishing it as a powerful and reliable tool for advanced statistical modelling in healthcare research.

摘要

对医学数据进行有效的分析对于理解复杂的医疗现象至关重要。概率分布模型提供了一种结构化方法来揭示此类数据中的模式，特别是用于研究疾病进展、生存分析等等。在本研究中，我们探索了一种通过将DUS变换应用于标准瑞利分布而导出的新型概率分布模型。我们深入研究了所提出模型的统计特性，并推导了关键可靠性度量以证明其在可靠性分析中的适用性。为确保精确的参数估计，我们评估了各种估计方法，并通过以偏差、均方误差和平均相对误差作为性能标准的详细模拟研究来评估它们的有效性。通过对COVID-19数据的分析，将所开发模型的性能与几种知名分布进行比较，证明了该模型的实际适用性。结果突出了该模型的灵活性和准确性，使其成为医疗保健研究中高级统计建模的强大而可靠的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/8840060b0f45/41598_2025_3645_Fig16_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/8840060b0f45/41598_2025_3645_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/629362463c74/41598_2025_3645_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/8c9b858b58b2/41598_2025_3645_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/8fa20e9b64e1/41598_2025_3645_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/809f74dbd8a0/41598_2025_3645_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/321aed24a790/41598_2025_3645_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/bf32afa892d5/41598_2025_3645_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/6bd888ebb96f/41598_2025_3645_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/d7f8e9809ca3/41598_2025_3645_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/524f/12117054/875e5f0a4571/41598_2025_3645_Fig14_HTML.jpg
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