Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) Book + PRICE WATCH * Amazon pricing is not included in price watch

Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) Book

This treatise by an acknowledged expert includes several topics not found in any previous book. This book shows how the central limit theorem for independent, identically distributed random variables with values in general, multidimensional...Read More

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  • Book Description

    The central limit theorem shows how, when samples become large, the probability laws of large numbers and related facts are guaranteed to hold over wide domains. This thorough treatise on the subject, by an acknowledged expert, includes several topics not found in any other book, such as the treatment of VC combinatorics, the proofs of a bootstrap central limit theorem and of invariance principles. It also includes problems at the end of each chapter. The book will interest mathematicians working in probability, mathematical statisticians, and computer scientists working in computer learning theory.

  • Product Description

    This book shows how, when samples become large, the probability laws of large numbers and related facts are guaranteed to hold over wide domains. The author, an acknowledged expert, gives a thorough treatment of the subject, including several topics not found in any previous book, such as the Fernique-Talagrand majorizing measure theorem for Gaussian processes, an extended treatment of Vapnik-Chervonenkis combinatorics, the Ossiander L2 bracketing central limit theorem, the Giné-Zinn bootstrap central limit theorem in probability, the Bronstein theorem on approximation of convex sets, and the Shor theorem on rates of convergence over lower layers. Other recent results of Talagrand and others are surveyed without proofs in separate sections. Problems are included at the end of each chapter so the book can be used as an advanced text. The book will interest mathematicians with an interest in probability, mathematical statisticians, and computer scientists working in computer learning theory.

  • 0521052211
  • 9780521052214
  • R. M. Dudley
  • 29 January 2008
  • Cambridge University Press
  • Paperback (Book)
  • 452
  • 1
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