Abstract The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese
neuroimaging genetics cohort and aims to identify genetic and environmental factors and …

Interpreting neural networks is a crucial and challenging task in machine learning. In this
paper, we develop a novel framework for detecting statistical interactions captured by a …

The concept of distance covariance/correlation was introduced recently to characterise
dependence among vectors of random variables. We review some statistical aspects of …

We introduce a very general method for high dimensional classification, based on careful
combination of the results of applying an arbitrary base classifier to random projections of …

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 …

Quadratic regression (QR) models naturally extend linear models by considering interaction
effects between the covariates. To conduct model selection in QR, it is important to maintain …

Hard thresholding rule is commonly adopted in feature screening procedures to screen out
unimportant predictors for ultrahigh-dimensional data. However, different thresholds are …

Railway turnout systems (RTS), as one of the most critical ground infrastructures of the high-
speed rail, which directly impact the railway operation safety, are also susceptible to …

Big data is ubiquitous in various fields of sciences, engineering, medicine, social sciences,
and humanities. It is often accompanied by a large number of variables and features. While …

Distance correlation has become an increasingly popular tool for detecting the nonlinear
dependence between a pair of potentially high-dimensional random vectors. Most existing …