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• W2510361369 abstract "The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic net, and many others. In this paper, we establish a general oracle inequality for prediction in high-dimensional linear regression with such methods. Since the proof relies only on convexity and continuity arguments, the result holds irrespective of the design matrix and applies to a wide range of penalized estimators. Overall, the bound demonstrates that generic estimators can provide consistent prediction with any design matrix. From a practical point of view, the bound can help to identify the potential of specific estimators, and they can help to get a sense of the prediction accuracy in a given application." @default.
• W2510361369 created "2016-09-16" @default.
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• W2510361369 date "2016-08-01" @default.
• W2510361369 modified "2023-09-27" @default.
• W2510361369 title "Oracle Inequalities for High-dimensional Prediction" @default.
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• W2510361369 hasPublicationYear "2016" @default.
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