Variable screening methods have been shown to be effective in dimension reduction under
the ultra-high dimensional setting. Most existing screening methods are designed to rank the …

This article is concerned with screening features in ultrahigh-dimensional data analysis,
which has become increasingly important in diverse scientific fields. We develop a sure …

In this article, we introduce a new methodology to perform feature screening for ultrahigh
dimensional data with multivariate responses. Several extant screening procedures are …

Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due
to complicated model structure and high noise level, existing screening methods often suffer …

Discussion We congratulate the authors for their thought-provoking and fascinating work on
a fundamental yet challenging topic in variable selection. Driven by the pressing need of …

Variable screening has been a useful research area that deals with ultrahigh-dimensional
data. When there exist both marginally and jointly dependent predictors to the response …

In this paper we introduce a modified Blum-Kiefer-Rosenblatt correlation (MBKR for short) to
rank the relative importance of each predictor in ultrahigh-dimensional regressions. We …

Advancements in technology have generated abundant high-dimensional data, enabling us
to integrate multiple relevant studies. In terms of variable selection, the significant …

Variable screening is a powerful and efficient tool for dimension reduction under ultrahigh
dimensional settings. However, most existing methods overlook useful prior knowledge in …

Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-
dimensional supervised problems with sparse signals; that is, a limited number of …