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 …

Variable selection in high-dimensional space characterizes many contemporary problems in
scientific discovery and decision making. Many frequently-used techniques are based on …

Ultrahigh dimensional data with both categorical responses and categorical covariates are
frequently encountered in the analysis of big data, for which feature screening has become …

This work is concerned with marginal sure independence feature screening for ultrahigh
dimensional discriminant analysis. The response variable is categorical in discriminant …

This article proposes a model-free and data-adaptive feature screening method for ultrahigh-
dimensional data. The proposed method is based on the projection correlation which …

In quantile linear regression with ultrahigh-dimensional data, we propose an algorithm for
screening all candidate variables and subsequently selecting relevant predictors …

We propose a method of factor profiled sure independence screening for ultrahigh-
dimensional variable selection. The objective of this method is to identify nonzero …

Ultrahigh-dimensional variable selection plays an increasingly important role in
contemporary scientific discoveries and statistical research. Among others, Fan and Lv [JR …

Motivated by the seminal theory of Sure Independence Screening (Fan and Lv 2008, SIS),
we investigate here another popular and classical variable screening method, namely …

We propose a Bayesian variable selection approach for ultrahigh dimensional linear
regression based on the strategy of split and merge. The approach proposed consists of two …