Listing 1 - 4 of 4 |
Sort by
|
Choose an application
Choose an application
This book shows how the central limit theorem for independent, identically distributed random variables with values in general, multidimensional spaces, holds uniformly over some large classes of functions. 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 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 working in probability, mathematical statisticians and computer scientists working in computer learning theory.
Choose an application
Ergodic theory. Information theory --- Central limit theorem --- Large deviations --- Markov processes --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Deviations, Large --- Limit theorems (Probability theory) --- Statistics --- Asymptotic distribution (Probability theory) --- Central limit theorem. --- Théorème de la limite centrale --- Markov processes. --- Markov, Processus de --- Large deviations. --- Grandes déviations --- Théorème de la limite centrale. --- Markov, Processus de. --- Grandes déviations.
Choose an application
Elements of Large-Sample Theory provides a unified treatment of first- order large-sample theory. It discusses a broad range of applications including introductions to density estimation, the bootstrap, and the asymptotics of survey methodology. The book is written at an elementary level and is suitable for students at the master's level in statistics and in aplied fields who have a background of two years of calculus. E.L. Lehmann is Professor of Statistics Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands, and the University of Chicago. Also available: Lehmann/Casella, Theory at Point Estimation, 2nd ed. Springer-Verlag New York, Inc., 1998, ISBN 0- 387-98502-6 Lehmann, Testing Statistical Hypotheses, 2nd ed. Springer-Verlag New York, Inc., 1997, ISBN 0-387-94919-4.
Sampling (Statistics) --- Asymptotic distribution (Probability theory) --- Law of large numbers. --- Distribution asymptotique (Théorie des probabilités) --- Loi des grands nombres --- Law of large numbers --- Asymptotic expansions --- Central limit theorem --- Distribution (Probability theory) --- Random sampling --- Statistics of sampling --- Large numbers, Law of --- Numbers, Large --- Convergence --- Asymptotic distribution (Probability theory). --- Distribution asymptotique (Théorie des probabilités) --- Statistics. --- Statistical Theory and Methods. --- Mathematical statistics --- Sampling (Statistics). --- Probabilities --- Statistics --- Echantillonnage (Statistique) --- EPUB-LIV-FT SPRINGER-B --- Mathematical statistics. --- Statistics . --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics
Listing 1 - 4 of 4 |
Sort by
|