论文标题
通过监督的回归系数降低维度：审查
Dimension Reduction via Supervised Clustering of Regression Coefficients: A Review
论文作者
论文摘要
缩小方法的发展和使用在现代统计文献中很普遍。本文回顾了一类缩小技术，旨在同时选择相关的预测因子，并在其中找到对响应有共同影响的群集。相对于OLS估计值和Lasso [Tibshirani，1996]，尤其是在存在预测因子中的多重共线性时，这种方法的性能相对于OLS估计值具有较高的性能。还讨论了它们的应用，包括遗传学，流行病学和功能磁共振成像研究。
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.
