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Uniform Central Limit Theorems

$46.00 ( ) USD

Part of Cambridge Studies in Advanced Mathematics

  • Date Published: April 2011
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9780511885174

$ 46.00 USD ( )
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About the Authors
  • 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.

    • Problems at the end of every chapter
    • Author is one of the world's experts in the subject
    • Applications in statistics, including a proof of the bootstrap central limit theorem
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    Reviews & endorsements

    "This monograph is a well-written treatise on functional central limit theorems...recommended for all who want to know more about a subject which by now is considered a must in abstract large-sample theory." Mathematical Reviews

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    Product details

    • Date Published: April 2011
    • format: Adobe eBook Reader
    • isbn: 9780511885174
    • contains: 2 b/w illus.
    • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • Table of Contents

    Preface
    1. Introduction: Donsker's theorem, metric entropy and inequalities
    2. Gaussian measures and processes
    sample continuity
    3. Foundations of uniform central limit theorems: Donsker classes
    4. Vapnik-Červonenkis combinatorics
    5. Measurability
    6. Limit theorems for Vapnik-Červonenkis and related classes
    7. Metric entropy, with inclusion and bracketing
    8. Approximation of functions and sets
    9. Sums in general Banach spaces and invariance principles
    10. Universal and uniform central limit theorems
    11. The two-sample case, the bootstrap, and confidence sets
    12. Classes of sets or functions too large for central limit theorems
    Appendices
    Subject index
    Author index
    Index of notation.

  • Author

    R. M. Dudley, Massachusetts Institute of Technology

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