Improved bounds for square-root Lasso and square-root slope. (English) Zbl 1473.62132

Summary: Extending the results of P. C. Bellec et al. [“Slope meets Lasso: improved oracle bounds and optimality”, Preprint, arXiv:1605.08651] to the setting of sparse high-dimensional linear regression with unknown variance, we show that two estimators, the Square-Root Lasso and the Square-Root Slope can achieve the optimal minimax prediction rate, which is \((s/n)\log\left (p/s\right )\), up to some constant, under some mild conditions on the design matrix. Here, \(n\) is the sample size, \(p\) is the dimension and \(s\) is the sparsity parameter. We also prove optimality for the estimation error in the \(l_{q}\)-norm, with \(q\in[1,2]\) for the Square-Root Lasso, and in the \(l_{2}\) and sorted \(l_{1}\) norms for the Square-Root Slope. Both estimators are adaptive to the unknown variance of the noise. The Square-Root Slope is also adaptive to the sparsity \(s\) of the true parameter. Next, we prove that any estimator depending on \(s\) which attains the minimax rate admits an adaptive to \(s\) version still attaining the same rate. We apply this result to the Square-root Lasso. Moreover, for both estimators, we obtain valid rates for a wide range of confidence levels, and improved concentration properties as in [loc. cit.] where the case of known variance is treated. Our results are non-asymptotic.


62G08 Nonparametric regression and quantile regression
62C20 Minimax procedures in statistical decision theory
62G05 Nonparametric estimation
62J05 Linear regression; mixed models
62J07 Ridge regression; shrinkage estimators (Lasso)
Full Text: DOI arXiv Euclid


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