High-dimensional data have frequently been collected in many scientific areas including
genomewide association study, biomedical imaging, tomography, tumor classifications, and …

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

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

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 …

The Hawkes process is a class of point processes whose future depends on their own
history. Previous theoretical work on the Hawkes process is limited to a special case in …

We propose two semiparametric model averaging schemes for nonlinear dynamic time
series regression models with a very large number of covariates including exogenous …

Nonparametric independence screening and structure identification for ultra-high dimensional
longitudinal data Page 1 The Annals of Statistics 2014, Vol. 42, No. 5, 1819–1849 DOI …

We consider the problem of modeling conditional independence structures in heterogenous
data in the presence of additional subject-level covariates—termed graphical regression …

Abstract Model average techniques are very useful for model-based prediction. However,
most earlier works in this field focused on parametric models and continuous responses. In …

Motivated by an empirical analysis of the Childhood Asthma Management Project, CAMP,
we introduce a new screening procedure for varying coefficient models with ultrahigh …