生物医学中统计助推的最新进展
An update on statistical boosting in biomedicine
摘要：
统计提升算法在过去十年中引发了大量的研究。它们将强大的机器学习方法与经典的统计建模相结合，提供了各种实际优势，如自动变量选择和效果估计的隐式正则化。它们非常灵活，因为底层的基本学习器（定义解释变量的效应类型的回归函数）可以与任何类型的损失函数（要优化的目标函数，定义回归设置的类型）相结合。在这篇综述文章中，我们重点介绍了关于变量选择，函数回归和高级时间事件建模的统计提升的最新方法学发展。此外，我们还简要介绍了统计提升在生物医学中的相关应用。
英文摘要：
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine-learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.
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