The development and use of dimension reduction methods is prevalent in modern statistical literature. This paper reviews a class of dimension reduction techniques which aim to simultaneously select relevant predictors and find clusters within them which share a common effect on the response. Such methods have been shown to have superior performance relative to OLS estimates and the lasso [Tibshirani, 1996] especially when multicollinearity in the predictors is present. Their applications, which include genetics, epidemiology, and fMRI studies, are also discussed.

翻译：在现代统计文献中,发展并使用减少维度的方法十分普遍,本文件回顾了一组减少维度的技术,这些技术旨在同时选择相关的预测器,并发现其中的组群对反应具有共同影响,这些方法与OSS估计值和Lasso[Tibshirani,1996年]相比表现优异,特别是在预测器存在多线性的情况下,还讨论了其应用,包括遗传学、流行病学和FMRI研究。