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

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 …

Alternative datasets are widely used for macroeconomic nowcasting together with machine
learning–based tools. The latter are often applied without a complete picture of their …

The aim of this article is to develop a low-rank linear regression model to correlate a high-
dimensional response matrix with a high-dimensional vector of covariates when coefficient …

In this article, we propose a model-free conditional feature screening method with false
discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built …

Abstract We propose the Factor Augmented (sparse linear) Regression Model (FARM) that
not only admits both the latent factor regression and sparse linear regression as special …

In this paper, we investigate the predictability of technical indicators on energy futures
volatility from the high-frequency and high-dimensional perspectives. We show that the …

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 …

We study a fully decentralized federated learning algorithm, which is a novel gradient
descent algorithm executed on a communication-based network. For convenience, we refer …

In neuroimaging studies, regression models are frequently used to identify the association of
the imaging features and clinical outcome, where the number of imaging features (eg …