High Dimensional Probability VI

The Banff Volume

  • Christian Houdré
  • David M. Mason
  • Jan Rosiński
  • Jon A. Wellner
Conference proceedings

Part of the Progress in Probability book series (PRPR, volume 66)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Inequalities and Convexity

  3. Limit Theorems

    1. Front Matter
      Pages 111-111
    2. Friedrich Götze, Alexandre Tikhomirov
      Pages 139-165
    3. James Kuelbs, Joel Zinn
      Pages 167-194
  4. Stochastic Processes

    1. Front Matter
      Pages 211-211
    2. Frank Aurzada, Tanja Kramm
      Pages 213-217
    3. Andreas Basse-O’Connor, Jan Rosiński
      Pages 219-225
  5. Random Matrices and Applications

    1. Front Matter
      Pages 245-245
    2. Hanna Döring, Peter Eichelsbacher
      Pages 261-275
  6. High Dimensional Statistics

    1. Front Matter
      Pages 303-303
    2. Vladimir Koltchinskii, Pedro Rangel
      Pages 305-325
    3. Dragan Radulovic
      Pages 357-373

About these proceedings


This is a collection of papers by participants at the High Dimensional Probability VI Meeting held from October 9-14, 2011 at the Banff International Research Station in Banff, Alberta, Canada. 

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 areas of mathematics, statistics, and computer science. These include random matrix theory, nonparametric statistics, empirical process theory, statistical learning theory, concentration of measure phenomena, strong and weak approximations, distribution function estimation in high dimensions, combinatorial optimization, and random graph theory.

The papers in this volume show that HDP theory continues to develop new tools, methods, techniques and perspectives to analyze the random phenomena. Both researchers and advanced students will find this book of great use for learning about new avenues of research.​


high dimensional probability limit theorems probability distributions

Editors and affiliations

  • Christian Houdré
    • 1
  • David M. Mason
    • 2
  • Jan Rosiński
    • 3
  • Jon A. Wellner
    • 4
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.University of DelawareNewarkUSA
  3. 3.University of TennesseeKnoxvilleUSA
  4. 4.University of WashingtonSeattleUSA

Bibliographic information