Nowadays, the growth of the high-throughput technologies has resulted in exponential
growth in the harvested data with respect to both dimensionality and sample size. The trend …

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

We propose robust methods for inference about the effect of a treatment variable on a scalar
outcome in the presence of very many regressors in a model with possibly non-Gaussian …

In this paper, we provide efficient estimators and honest confidence bands for a variety of
treatment effects including local average (LATE) and local quantile treatment effects (LQTE) …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

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

Variable selection plays an important role in high dimensional statistical modelling which
nowadays appears in many areas and is key to various scientific discoveries. For problems …

We propose a pivotal method for estimating high-dimensional sparse linear regression
models, where the overall number of regressors p is large, possibly much larger than n, but …

Grouping structures arise naturally in many statistical modeling problems. Several methods
have been proposed for variable selection that respect grouping structure in variables …

Ultrahigh-dimensional variable selection plays an increasingly important role in
contemporary scientific discoveries and statistical research. Among others, Fan and Lv [JR …