High Dimensional Probability VII

This volume collects selected papers from the 7th High Dimensional Probability meeting held at the Institut d'Études Scientifiques de Cargèse (IESC) in Corsica, France.

High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, and random graphs.

The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques andperspectives to analyze random phenomena.

Gives a unique view on the mathematical methods used by experts to establish high dimensional results Displays the wide scope of the types of problems to which these methods can be successfully applied Provides not only a valuable introduction to what is meant by high dimensional probability, but also exposes fruitful new areas of research Includes supplementary material: sn.pub/extras

Inhalt
Dedication to Evarist Gine-Masdeu.- Inequalities and Convexity.- Limit Theorems.- Stochastic Processes.- High Dimensional Statistics.