Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …

We review statistical methods for high-dimensional data analysis and pay particular
attention to recent developments for assessing uncertainties in terms of controlling false …

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

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

We propose a new sparsity-smoothness penalty for high-dimensional generalized additive
models. The combination of sparsity and smoothness is crucial for mathematical theory as …

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 © …

We propose a method for constructing p-values for general hypotheses in a high-
dimensional linear model. The hypotheses can be local for testing a single regression …

This article considers high-dimensional regression problems in which the number of
predictors p exceeds the sample size n. We develop a model-averaging procedure for high …

Most papers on high-dimensional statistics are based on the assumption that none of the
regressors are correlated with the regression error, namely, they are exogenous. Yet …

Grain yield progress over 50 years of spring wheat breeding at the International Maize and
Wheat Improvement Center (CIMMYT) was determined in field trials conducted during five …