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 …

This article reviews the literature on sparse high-dimensional models and discusses some
applications in economics and finance. Recent developments in theory, methods, and …

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 …

A variable screening procedure via correlation learning was proposed by Fan and Lv (2008)
to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true …

This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …

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 © …

Variance estimation is a fundamental problem in statistical modelling. In ultrahigh
dimensional linear regression where the dimensionality is much larger than the sample size …

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 …

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 …