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- W4297819285 abstract "Sparse linear regression methods generally have a free hyperparameter which controls the amount of sparsity, and is subject to a bias-variance tradeoff. This article considers the use of Aggregated hold-out to aggregate over values of this hyperparameter, in the context of linear regression with the Huber loss function. Aggregated hold-out (Agghoo) is a procedure which averages estimators selected by hold-out (cross-validation with a single split). In the theoretical part of the article, it is proved that Agghoo satisfies a non-asymptotic oracle inequality when it is applied to sparse estimators which are parametrized by their zero-norm. In particular , this includes a variant of the Lasso introduced by Zou, Hasti{'e} and Tibshirani. Simulations are used to compare Agghoo with cross-validation. They show that Agghoo performs better than CV when the intrinsic dimension is high and when there are confounders correlated with the predictive covariates." @default.
- W4297819285 created "2022-10-01" @default.
- W4297819285 creator A5014993545 @default.
- W4297819285 date "2020-02-26" @default.
- W4297819285 modified "2023-10-16" @default.
- W4297819285 title "Aggregated hold out for sparse linear regression with a robust loss function" @default.
- W4297819285 doi "https://doi.org/10.48550/arxiv.2002.11553" @default.
- W4297819285 hasPublicationYear "2020" @default.
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