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用于估计威布尔竞争风险模型参数的期望最大化(EM)算法

EM Algorithm for Estimating the Parameters of Weibull Competing Risk Model.

作者信息

Kayid Mohamed

机构信息

Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia.

出版信息

Appl Bionics Biomech. 2021 Oct 21;2021:1179856. doi: 10.1155/2021/1179856. eCollection 2021.

Abstract

One of the most commonly used models in survival analysis is the additive Weibull model and its generalizations. They are well suited for modeling bathtub-shaped hazard rates that are a natural form of the hazard rate. Although they have some advantages, the maximum likelihood and the least square estimators are biased and have poor performance when the data set contains a large number of parameters. As an alternative, the expectation-maximization (EM) algorithm was applied to estimate the parameters of the additive Weibull model. The accuracy of the parameter estimates and the simulation study confirmed the advantages of the EM algorithm.

摘要

生存分析中最常用的模型之一是加法威布尔模型及其推广形式。它们非常适合对浴盆形危险率进行建模,而浴盆形危险率是危险率的一种自然形式。尽管它们有一些优点,但当数据集包含大量参数时,最大似然估计和最小二乘估计是有偏的,并且性能不佳。作为一种替代方法,期望最大化(EM)算法被应用于估计加法威布尔模型的参数。参数估计的准确性和模拟研究证实了EM算法的优点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50c4/8553452/9490bfb97dab/ABB2021-1179856.001.jpg

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