Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The pattern of zero entries in the inverse covariance matrix of a multivariate normal
distribution corresponds to conditional independence restrictions between variables …

We show that, under a sparsity scenario, the Lasso estimator and the Dantzig selector
exhibit similar behavior. For both methods, we derive, in parallel, oracle inequalities for the …

We present a new class of methods for high dimensional non-parametric regression and
classification called sparse additive models. Our methods combine ideas from sparse linear …

Sparse PCA: Optimal rates and adaptive estimation Page 1 The Annals of Statistics 2013, Vol.
41, No. 6, 3074–3110 DOI: 10.1214/13-AOS1178 © Institute of Mathematical Statistics, 2013 …

We consider the least-square regression problem with regularization by a block l1-norm, that
is, a sum of Euclidean norms over spaces of dimensions larger than one. This problem …

We consider the problem of obtaining appropriate weights for averaging M approximate
(misspecified) models for improved estimation of an unknown conditional mean in the face …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

This paper studies oracle properties of ℓ 1-penalized least squares in nonparametric
regression setting with random design. We show that the penalized least squares estimator …