Big Data bring new opportunities to modern society and challenges to data scientists. On the
one hand, Big Data hold great promises for discovering subtle population patterns and …

In this paper, we investigate several variable selection procedures to give an overview of the
existing literature for practitioners.“Let the data speak for themselves” has become the motto …

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 …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

Robust rank correlation based screening Page 1 The Annals of Statistics 2012, Vol. 40, No. 3,
1846–1877 DOI: 10.1214/12-AOS1024 © Institute of Mathematical Statistics, 2012 ROBUST …

Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …

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

This article is concerned with feature screening and variable selection for varying coefficient
models with ultrahigh-dimensional covariates. We propose a new feature screening …

The varying coefficient model is an important class of nonparametric statistical model, which
allows us to examine how the effects of covariates vary with exposure variables. When the …

Global sensitivity analysis with variance-based measures suffers from several theoretical
and practical limitations, since they focus only on the variance of the output and handle …