Uniform central limit theorems /
Material type: TextSeries: Cambridge studies in advanced mathematicsPublication details: New York Cambridge University 2014Edition: 2nd edDescription: xii, 472 pagesISBN: 9780521498845 (hardback)Subject(s): Central limit theorem | Mathematics Probability & StatisticsDDC classification: 519.2 Online resources: Not Available Summary: "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. 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, Gine; 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"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
BK | Stack | Stack | 519.2 DUD/U (Browse shelf (Opens below)) | Available | 59427 |
Browsing Kannur University Central Library shelves, Shelving location: Stack, Collection: Stack Close shelf browser (Hides shelf browser)
519.2 BRB/M Modern probability theory : an introductory textbook | 519.2 CHO/P Probability theory : independence, interchangeability, martingales | 519.2 CHU/C Course in probability theory | 519.2 DUD/U Uniform central limit theorems / | 519.2 FAL/U Understanding probability and statistics : a book of problems | 519.2 GOR/C Classic problemsm of probability | 519.2 GUP/P Probability Theory-The Logic of Science. |
"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. 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, Gine; 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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