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 …

High dimensional statistical problems arise from diverse fields of scientific research and
technological development. Variable selection plays a pivotal role in contemporary statistical …

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

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 …

With the recent explosion of scientific data of unprecedented size and complexity, feature
ranking and screening are playing an increasingly important role in many scientific studies …

This paper studies hypothesis testing and parameter estimation in the context of the divide-
and-conquer algorithm. In a unified likelihood based framework, we propose new test …

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 …

Determining how to select the tuning parameter appropriately is essential in penalized
likelihood methods for high dimensional data analysis. We examine this problem in the …

Variable selection in high-dimensional space characterizes many contemporary problems in
scientific discovery and decision making. Many frequently-used techniques are based on …

Folded concave penalization methods have been shown to enjoy the strong oracle property
for high-dimensional sparse estimation. However, a folded concave penalization problem …