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

We give an overview of random matrix theory (RMT) with the objective of highlighting the
results and concepts that have a growing impact in the formulation and inference of …

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear
Operators provides a uniquely broad compendium of the key mathematical concepts and …

The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

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

We provide a unified analysis of the predictive risk of ridge regression and regularized
discriminant analysis in a dense random effects model. We work in a high-dimensional …

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 …

Estimating causal effects under exogeneity hinges on two key assumptions:
unconfoundedness and overlap. Researchers often argue that unconfoundedness is more …

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 …

Modern Directional Statistics collects important advances in methodology and theory for
directional statistics over the last two decades. It provides a detailed overview and analysis …