期刊
ANNALS OF STATISTICS
卷 36, 期 2, 页码 614-645出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/009053607000000929
关键词
lasso; oracle inequality; sparsity
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.
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