High Dimensional Probability VII : The Cargèse Volume / edited by Christian Houdré, David M. Mason, Patricia Reynaud-Bouret, Jan Rosiński.

Format:

Book

Contributor:

Houdré, Christian., Editor.

Mason, David M., Editor.

Reynaud-Bouret, Patricia., Editor.

Rosiński, Jan., Editor.

Series:

Progress in Probability, 2297-0428 ; 71

Language:

English

Subjects (All):

Probabilities.

Probability Theory.

Local Subjects:

Probability Theory.

Physical Description:

1 online resource (XXVIII, 461 p.)

Edition:

1st ed. 2016.

Place of Publication:

Cham : Springer International Publishing : Imprint: Birkhäuser, 2016.

Summary:

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 and perspectives to analyze random phenomena.

Contents:

Dedication to Evarist Gine-Masdeu

Inequalities and Convexity

Limit Theorems

Stochastic Processes

High Dimensional Statistics.

Notes:

Includes bibliographical references at the end of each chapters.

ISBN:

3-319-40519-5