In this article, we propose a new metric, the so-called martingale difference correlation, to
measure the departure of conditional mean independence between a scalar response …

Independence screening is powerful for variable selection when the number of variables is
massive. Commonly used independence screening methods are based on marginal …

Feature screening and variable selection are fundamental in analysis of ultrahigh-
dimensional data, which are being collected in diverse scientific fields at relatively low cost …

Variable selection is a challenging issue in statistical applications when the number of
predictors p far exceeds the number of observations n. In this ultrahigh dimensional setting …

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

We introduce the partial martingale difference correlation, a scalar-valued measure of
conditional mean dependence of Y given X, adjusting for the nonlinear dependence on Z …

Extracting important features from ultra-high dimensional data is one of the primary tasks in
statistical learning, information theory, precision medicine, and biological discovery. Many of …

We discuss briefly the very interesting concept of Brownian distance covariance developed
by Székely and Rizzo [Ann. Appl. Statist.(2009), to appear] and describe two possible …

Motivated by applications in biological science, we propose a novel test to assess the
conditional mean dependence of a response variable on a large number of covariates. Our …

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