标题： 高维广义线性模型与套索（lasso） 显示英文标题

标题： High-dimensional generalized linear models and the lasso

摘要： 我们研究了具有Lipschitz损失函数的高维广义线性模型，并证明了经验风险最小化器（带有Lasso惩罚）的一个非渐近oracle不等式。 该惩罚基于线性预测器中的系数，在经验范数归一化后得到。例子包括逻辑回归、密度估计和hinge损失下的分类。 最小二乘回归也进行了讨论。 显示英文摘要

摘要： We consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso penalty. The penalty is based on the coefficients in the linear predictor, after normalization with the empirical norm. The examples include logistic regression, density estimation and classification with hinge loss. Least squares regression is also discussed.