Grouping structures arise naturally in many statistical modeling problems. Several methods
have been proposed for variable selection that respect grouping structure in variables …

We review statistical methods for high-dimensional data analysis and pay particular
attention to recent developments for assessing uncertainties in terms of controlling false …

There are many settings where researchers are interested in estimating average treatment
effects and are willing to rely on the unconfoundedness assumption, which requires that the …

On asymptotically optimal confidence regions and tests for high-dimensional models Page 1
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …

Fitting high-dimensional statistical models often requires the use of non-linear parameter
estimation procedures. As a consequence, it is generally impossible to obtain an exact …

The purpose of this paper is to propose methodologies for statistical inference of low
dimensional parameters with high dimensional data. We focus on constructing confidence …

Regularized estimation in sparse high-dimensional time series models Page 1 The Annals
of Statistics 2015, Vol. 43, No. 4, 1535–1567 DOI: 10.1214/15-AOS1315 © Institute of …

In applications throughout science and engineering one is often faced with the challenge of
solving an ill-posed inverse problem, where the number of available measurements is …

The lasso is a popular tool for sparse linear regression, especially for problems in which the
number of variables p exceeds the number of observations n. But when p>n, the lasso …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …