Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 11, Number 1 (2017), 2519-2546.
Optimal two-step prediction in regression
High-dimensional prediction typically comprises two steps: variable selection and subsequent least-squares refitting on the selected variables. However, the standard variable selection procedures, such as the lasso, hinge on tuning parameters that need to be calibrated. Cross-validation, the most popular calibration scheme, is computationally costly and lacks finite sample guarantees. In this paper, we introduce an alternative scheme, easy to implement and both computationally and theoretically efficient.
Electron. J. Statist. Volume 11, Number 1 (2017), 2519-2546.
Received: May 2016
First available in Project Euclid: 2 June 2017
Permanent link to this document
Digital Object Identifier
Chételat, Didier; Lederer, Johannes; Salmon, Joseph. Optimal two-step prediction in regression. Electron. J. Statist. 11 (2017), no. 1, 2519--2546. doi:10.1214/17-EJS1287. https://projecteuclid.org/euclid.ejs/1496390437