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 …

Despite of various similar features, Functional Data Analysis and High-Dimensional Data
Analysis are two major fields in Statistics that grew up recently almost independently one …

This paper considers estimation and prediction of a high-dimensional linear regression in
the setting of transfer learning where, in addition to observations from the target model …

One of the central tasks of genome research is to predict phenotypes and discover some
important gene biomarkers. However, there are three main problems in analyzing genomics …

Concentration inequalities form an essential toolkit in the study of high-dimensional
statistical methods. Most of the relevant statistics literature in this regard is, however, based …

Many causal and structural effects depend on regressions. Examples include policy effects,
average derivatives, regression decompositions, average treatment effects, causal …

We consider the problem of signal estimation in generalized linear models defined via
rotationally invariant design matrices. Since these matrices can have an arbitrary spectral …

Objectives The purpose of this study is to describe the situation of COVID-19 in European
countries and to identify important factors related to prevention and control. Methods We …

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …

We propose and study a class of simple, nonparametric, yet interpretable measures of
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …