High-Dimensional Generalized Linear Models and the Lasso
High-Dimensional Generalized Linear Models and the Lasso
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Publisher
Hayward, CA: Institute of Mathematical Statistics
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Language
English
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Publisher
Hayward, CA: Institute of Mathematical Statistics
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Contents
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 estima...
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Full title
High-Dimensional Generalized Linear Models and the Lasso
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TN_cdi_projecteuclid_primary_oai_CULeuclid_euclid_aos_1205420513
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_projecteuclid_primary_oai_CULeuclid_euclid_aos_1205420513
Other Identifiers
ISSN
0090-5364
E-ISSN
2168-8966
DOI
10.1214/009053607000000929
