The Electronic Journal of Statistics, Volume 10, number 2, contains a Special Section entitled “Statistical Inference in Sparse High-Dimensional Models”. This special section has been co-edited by Florentina Bunea and Marloes Maathuis and contains invited papers that originated from the 2013–2014 SAMSI program on Low-Dimensional Structures in High-Dimensional Systems, and in particular from the mid-program workshop, “Statistical Inference in Sparse High-Dimensional Models: Theoretical and Computational Challenges”. It includes the following papers with original contributions in the areas of empirical process theory, high-dimensional regularized regression (estimation and inference), matrix estimation, Bayesian nonparametric regression, and optimization:

Bounding the expectation of the supremum of an empirical process over a (weak) VC-major class …Yannick Baraud; 1709–1728

An {ℓ1, ℓ2, ℓ∞}-regularization approach to high-dimensional errors-in-variables models … Alexandre Belloni, Mathieu Rosenbaum, and Alexandre B. Tsybakov; 1729–1750

Bernstein-von Mises theorems for functionals of the covariance matrix … Chao Gao and Harrison H. Zhou; 1751–1806

Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks … Tristan Gray-Davies, Chris C. Holmes, and François Caron; 1807–1828

The benefit of group sparsity in group inference with de-biased scaled group Lasso … Ritwik Mitra and Cun-Hui Zhang; 1829–1873

On the finite-sample analysis of Θ-estimators … Yiyuan She; 1874–1895

You can read EJS Volume 10, number 2, via Project Euclid at http://projecteuclid.org/euclid.ejs/1468849960