We consider rules for discarding predictors in lasso regression and related problems, for
computational efficiency. El Ghaoui and his colleagues have proposed 'SAFE'rules, based …

In many important statistical applications, the number of variables or parameters p is much
larger than the number of observations n. Suppose then that we have observations y= Xβ+ z …

Extraordinary amounts of data are being produced in many branches of science. Proven
statistical methods are no longer applicable with extraordinary large datasets due to …

Supplementary material for Least squares after model selection in high-dimensional sparse
models. The online supplemental article 2 contains a finite sample results for the estimation …

We describe an R package named huge which provides easy-to-use functions for estimating
high dimensional undirected graphs from data. This package implements recent results in …

With high-dimensional data, variable-by-variable statistical testing is often used to select
variables whose behavior differs across conditions. Such an approach requires adjustment …

High dimensional statistical problems arise from diverse fields of scientific research and
technological development. Variable selection plays a pivotal role in contemporary statistical …

We consider the problem of model selection and estimation in situations where the number
of parameters diverges with the sample size. When the dimension is high, an ideal method …

Scaled sparse linear regression jointly estimates the regression coefficients and noise level
in a linear model. It chooses an equilibrium with a sparse regression method by iteratively …

With the recent explosion of scientific data of unprecedented size and complexity, feature
ranking and screening are playing an increasingly important role in many scientific studies …