High-dimensional regression with unknown variance
arXiv:1109.5587
Abstract
We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasis is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso estimator and some references are collected for some more general models, including multivariate regression and nonparametric regression.
38 pages
References in corpus (7)
- Nearly unbiased variable selection under minimax concave penalty
- Consistency of the group Lasso and multiple kernel learning
- Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
- Consistency of trace norm minimization
- Scaled Sparse Linear Regression
- Sparsity regret bounds for individual sequences in online linear regression
- A pseudo-RIP for multivariate regression