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 …

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

Abstract Motivation Linear mixed models (LMMs) have long been the method of choice for
risk prediction analysis on high-dimensional data. However, it remains computationally …

A model-free feature screening method for ultrahigh-dimensional right-censored data is
advocated. A two-step approach, with the help of knockoff features, is proposed to specify …

Distance covariance is a powerful new dependence measure that was recently introduced
by Székely et al. and Székely and Rizzo. In this work, the concept of distance covariance is …

Variable selection for interval‐censored failure time data has recently attracted a great deal
of attention along with the analysis of interval‐censored data in both method developments …

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

A prevalent feature of high-dimensional data is the dependence among covariates, and
model selection is known to be challenging when covariates are highly correlated. To …

This article is concerned with a non-marginal feature screening procedure for varying
coefficient competing risks models with ultra-high dimensional covariates. The proposed …

In this paper, we propose a new measure, called Projection Quantile Correlation (PQC), to
detect quantile dependence between a response and multivariate predictors at a given …