The limitations of the well‐known LASSO regression as a variable selector are tested when
there exists dependence structures among covariates. We analyse both the classic situation …

This review article gives a high-level overview of the approaches across different scales of
organization and levels of abstraction. The studies covered in this paper include …

Meeting the Paris Agreement's climate targets necessitates better knowledge about which
climate policies work in reducing emissions at the necessary scale. We provide a global …

Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes,
lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically …

Students in statistics or data science usually learn early on that when the sample size n is
large relative to the number of variables p, fitting a logistic model by the method of maximum …

Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …

An increasing number of machine learning models have been deployed in domains with
high stakes such as finance and healthcare. Despite their superior performances, many …

Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

Regularized regression problems are ubiquitous in statistical modeling, signal processing,
and machine learning. Sparse regression, in particular, has been instrumental in scientific …

The Lasso with general Gaussian designs with applications to hypothesis testing Page 1 The
Annals of Statistics 2023, Vol. 51, No. 5, 2194–2220 https://doi.org/10.1214/23-AOS2327 © …