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Uniform Central Limit Theorems (Cambridge Studies in Advanced Mathematics) [Paperback]
R. M. Dudley (Author)
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Book Description
This second edition of the classic work on empirical processes has been considerably expanded and revised. It now includes complete proofs of all results, including several new theorems not included in the first edition, such as Talagrand's generic chaining approach to boundedness of Gaussian processes and Giné and Zinn's characterization of uniform Donsker classes.
About the Author
R. M. Dudley is a Professor of Mathematics at the Massachusetts Institute of Technology in Cambridge, Massachusetts.
Product Details
- Series: Cambridge Studies in Advanced Mathematics (Book 142)
- Paperback: 488 pages
- Publisher: Cambridge University Press; 2 edition (January 31, 2014)
- Language: English
- ISBN-10: 0521738415
- ISBN-13: 978-0521738415
Book Description
Publication Date: January 31, 2014 | ISBN-10: 0521738415 | ISBN-13: 978-0521738415 | Edition: 2
This classic work on empirical processes has been considerably expanded and revised from the original edition. When samples become large, the probability laws of large numbers and central limit theorems are guaranteed to hold uniformly over wide domains. The author, an acknowledged expert, gives a thorough treatment of the subject, including 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. This new edition contains several proved theorems not included in the first edition, including the Bretagnolle-Massart theorem giving constants in the Komlos-Major-Tusnady rate of convergence for the classical empirical process, Massart's form of the Dvoretzky-Kiefer-Wolfowitz inequality with precise constant, Talagrand's generic chaining approach to boundedness of Gaussian processes, a characterization of uniform Glivenko-Cantelli classes of functions, Giné and Zinn's characterization of uniform Donsker classes (i.e., classing Donsker uniformly over all probability measures P), and the Bousquet-Koltchinskii-Panchenko theorem that the convex hull of a uniform Donsker class is uniform Donsker. The book will be an essential reference for mathematicians working in infinite-dimensional central limit theorems, mathematical statisticians, and computer scientists working in computer learning theory. Problems are included at the end of each chapter so the book can also be used as an advanced text.
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