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基于增强瑞利-逆威布尔模型的生存分析。

Survival analysis based on an enhanced Rayleigh-inverted Weibull model.

作者信息

Elgarhy Mohammed, Kayid Mohamed, Johannssen Arne, Elsehetry Mahmoud

机构信息

Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef 62521, Egypt.

Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis, AlSharkia, Egypt.

出版信息

Heliyon. 2024 Aug 10;10(16):e35851. doi: 10.1016/j.heliyon.2024.e35851. eCollection 2024 Aug 30.

DOI:10.1016/j.heliyon.2024.e35851
PMID:39687856
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11647869/
Abstract

This study proposes a two-parameter survival model based on the Kavya-Manoharan transformation family and the Rayleigh-inverted Weibull distribution, the so-called Kavya-Manoharan-Rayleigh inverted Weibull distribution (KMRIWD). Various reliability measures and statistical properties of this distribution are analyzed. The parameters of the distribution are estimated using the maximum likelihood method and different sampling techniques. Using Monte Carlo simulations, the performance of the estimators is evaluated and compared. Finally, the model and numerous competitors are compared using real data sets, and it is shown that the KMRIWD has a better fit than all the competitors.

摘要

本研究提出了一种基于卡维亚 - 马诺哈兰变换族和瑞利 - 逆威布尔分布的双参数生存模型，即所谓的卡维亚 - 马诺哈兰 - 瑞利逆威布尔分布（KMRIWD）。分析了该分布的各种可靠性度量和统计特性。使用最大似然法和不同的抽样技术估计分布的参数。通过蒙特卡罗模拟，评估并比较了估计量的性能。最后，使用真实数据集将该模型与众多竞争模型进行比较，结果表明KMRIWD比所有竞争模型具有更好的拟合度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/0e0ccbc0c155/gr012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/149fce649811/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/ca9b1d428867/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/72c313439169/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/25b0d01d897f/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/bda3fab01407/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/2ecd1d84d95f/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/8025479d0d09/gr009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/c6e9813eef75/gr010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/6381a5e4c6f2/gr011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/0e0ccbc0c155/gr012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/149fce649811/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/ca9b1d428867/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/72c313439169/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/25b0d01d897f/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/bda3fab01407/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/2ecd1d84d95f/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/8025479d0d09/gr009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/c6e9813eef75/gr010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/6381a5e4c6f2/gr011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a32/11647869/0e0ccbc0c155/gr012.jpg

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本文引用的文献

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A statistical framework for a new Kavya-Manoharan Bilal distribution using ranked set sampling and simple random sampling.一种使用排序集抽样和简单随机抽样的新型卡维亚 - 马诺哈兰·比拉尔分布的统计框架。
Heliyon. 2024 May 4;10(9):e30762. doi: 10.1016/j.heliyon.2024.e30762. eCollection 2024 May 15.
2
A new Topp-Leone Kumaraswamy Marshall-Olkin generated family of distributions with applications.一个新的由Topp-Leone Kumaraswamy Marshall-Olkin生成的分布族及其应用
Heliyon. 2024 Jan 8;10(2):e24001. doi: 10.1016/j.heliyon.2024.e24001. eCollection 2024 Jan 30.
3
Statistical Inference under Censored Data for the New Exponential-X Fréchet Distribution: Simulation and Application to Leukemia Data.
在删失数据下的新指数-X Fréchet 分布的统计推断：对白血病数据的模拟和应用。
Comput Intell Neurosci. 2021 Aug 29;2021:2167670. doi: 10.1155/2021/2167670. eCollection 2021.
4
Exponentiated power generalized Weibull power series family of distributions: Properties, estimation and applications.指数幂广义威布尔幂级数分布族：性质、估计与应用。
PLoS One. 2020 Mar 20;15(3):e0230004. doi: 10.1371/journal.pone.0230004. eCollection 2020.
5
Using ranked set sampling with extreme ranks in estimating the population proportion.采用极端秩的等级抽样估计总体比例。
Stat Methods Med Res. 2020 Jan;29(1):165-177. doi: 10.1177/0962280218823793. Epub 2019 Feb 1.