Logo

Convergence of Stochastic Processes

Large book cover: Convergence of Stochastic Processes

Convergence of Stochastic Processes
by

Publisher: Springer
ISBN/ASIN: 1461297583
ISBN-13: 9781461297581
Number of pages: 223

Description:
An exposition od selected parts of empirical process theory, with related interesting facts about weak convergence, and applications to mathematical statistics. The high points of the book describe the combinatorial ideas needed to prove maximal inequalities for empirical processes indexed by classes of sets or classes of functions.

Home page url

Download or read it online for free here:
Download link
(8.6MB, PDF)

Similar books

Book cover: Lectures on Noise Sensitivity and PercolationLectures on Noise Sensitivity and Percolation
by - arXiv
The goal of this set of lectures is to combine two seemingly unrelated topics: (1) The study of Boolean functions, a field particularly active in computer science; (2) Some models in statistical physics, mostly percolation.
(14698 views)
Book cover: Lectures on Probability, Statistics and EconometricsLectures on Probability, Statistics and Econometrics
by - statlect.com
This e-book is organized as a website that provides access to a series of lectures on fundamentals of probability, statistics and econometrics, as well as to a number of exercises on the same topics. The level is intermediate.
(17609 views)
Book cover: Probability and Statistics CookbookProbability and Statistics Cookbook
by
The cookbook contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive reference reduced to the mathematical essence, rather than aiming for elaborate explanations.
(22550 views)
Book cover: A defense of Columbo: A multilevel introduction to probabilistic reasoningA defense of Columbo: A multilevel introduction to probabilistic reasoning
by - arXiv
Triggered by a recent interesting article on the too frequent incorrect use of probabilistic evidence in courts, the author introduces the basic concepts of probabilistic inference with a toy model, and discusses several important issues.
(19169 views)