Moderately clipped LASSO for the high-dimensional generalized linear model
نویسندگان
چکیده
منابع مشابه
High - Dimensional Generalized Linear Models and the Lasso
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 ...
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ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2020
ISSN: 2383-4757
DOI: 10.29220/csam.2020.27.4.445