Empirical Processes With Applications To Statistics
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Empirical Processes with Applications to Statistics
Author | : Galen R. Shorack,Jon A. Wellner |
Publsiher | : SIAM |
Total Pages | : 992 |
Release | : 2009-01-01 |
Genre | : Mathematics |
ISBN | : 9780898719017 |
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Originally published in 1986, this valuable reference provides a detailed treatment of limit theorems and inequalities for empirical processes of real-valued random variables; applications of the theory to censored data, spacings, rank statistics, quantiles, and many functionals of empirical processes, including a treatment of bootstrap methods; and a summary of inequalities that are useful for proving limit theorems. At the end of the Errata section, the authors have supplied references to solutions for 11 of the 19 Open Questions provided in the book's original edition. Audience: researchers in statistical theory, probability theory, biostatistics, econometrics, and computer science.
Introduction to Empirical Processes and Semiparametric Inference
Author | : Michael R. Kosorok |
Publsiher | : Springer Science & Business Media |
Total Pages | : 483 |
Release | : 2007-12-29 |
Genre | : Mathematics |
ISBN | : 9780387749785 |
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Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
Empirical Processes
Author | : David Pollard |
Publsiher | : IMS |
Total Pages | : 100 |
Release | : 1990 |
Genre | : Distribution (Probability theory). |
ISBN | : 0940600161 |
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Empirical Process Techniques for Dependent Data
Author | : Herold Dehling,Thomas Mikosch,Michael Sörensen |
Publsiher | : Springer Science & Business Media |
Total Pages | : 378 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 9781461200994 |
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Empirical process techniques for independent data have been used for many years in statistics and probability theory. These techniques have proved very useful for studying asymptotic properties of parametric as well as non-parametric statistical procedures. Recently, the need to model the dependence structure in data sets from many different subject areas such as finance, insurance, and telecommunications has led to new developments concerning the empirical distribution function and the empirical process for dependent, mostly stationary sequences. This work gives an introduction to this new theory of empirical process techniques, which has so far been scattered in the statistical and probabilistic literature, and surveys the most recent developments in various related fields. Key features: A thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data * Accessible surveys by leading experts of the most recent developments in various related fields * Examines empirical process techniques for dependent data, useful for studying parametric and non-parametric statistical procedures * Comprehensive bibliographies * An overview of applications in various fields related to empirical processes: e.g., spectral analysis of time-series, the bootstrap for stationary sequences, extreme value theory, and the empirical process for mixing dependent observations, including the case of strong dependence. To date this book is the only comprehensive treatment of the topic in book literature. It is an ideal introductory text that will serve as a reference or resource for classroom use in the areas of statistics, time-series analysis, extreme value theory, point process theory, and applied probability theory. Contributors: P. Ango Nze, M.A. Arcones, I. Berkes, R. Dahlhaus, J. Dedecker, H.G. Dehling,
Convergence of Stochastic Processes
Author | : D. Pollard |
Publsiher | : David Pollard |
Total Pages | : 223 |
Release | : 1984-10-08 |
Genre | : Mathematics |
ISBN | : 9780387909905 |
Download Convergence of Stochastic Processes Book in PDF, Epub and Kindle
Functionals on stochastic processes; Uniform convergence of empirical measures; Convergence in distribution in euclidean spaces; Convergence in distribution in metric spaces; The uniform metric on space of cadlag functions; The skorohod metric on D [0, oo); Central limit teorems; Martingales.
Empirical Processes in M Estimation
Author | : Sara A. Geer |
Publsiher | : Cambridge University Press |
Total Pages | : 302 |
Release | : 2000-01-28 |
Genre | : Business & Economics |
ISBN | : 052165002X |
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Advanced text; estimation methods in statistics, e.g. least squares; lots of examples; minimal abstraction.
Weak Convergence and Empirical Processes
Author | : Aad van der vaart,Jon Wellner |
Publsiher | : Springer Science & Business Media |
Total Pages | : 523 |
Release | : 2013-03-09 |
Genre | : Mathematics |
ISBN | : 9781475725452 |
Download Weak Convergence and Empirical Processes Book in PDF, Epub and Kindle
This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists. Part three covers a range of topics demonstrating the applicability of the theory to key questions such as measures of goodness of fit and the bootstrap.
Principles of Nonparametric Learning
Author | : Laszlo Györfi |
Publsiher | : Springer |
Total Pages | : 344 |
Release | : 2014-05-04 |
Genre | : Technology & Engineering |
ISBN | : 9783709125687 |
Download Principles of Nonparametric Learning Book in PDF, Epub and Kindle
This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation, and genetic programming.