- Volume 25, Number 2 (2019), 1225-1255.
Oracle inequalities for high-dimensional prediction
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.
Bernoulli, Volume 25, Number 2 (2019), 1225-1255.
Received: April 2017
Revised: December 2017
First available in Project Euclid: 6 March 2019
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Lederer, Johannes; Yu, Lu; Gaynanova, Irina. Oracle inequalities for high-dimensional prediction. Bernoulli 25 (2019), no. 2, 1225--1255. doi:10.3150/18-BEJ1019. https://projecteuclid.org/euclid.bj/1551862849