Stable Non-Gaussian Random Processes
Title | Stable Non-Gaussian Random Processes PDF eBook |
Author | Gennady Samorodnitsky |
Publisher | CRC Press |
Pages | 662 |
Release | 1994-06-01 |
Genre | Mathematics |
ISBN | 9780412051715 |
Both an introduction and a basic reference text on non-Gaussian stable models, for graduate students and practitioners. Assuming only a first-year graduate course in probability, it includes material which has only recently appeared in journals and unpublished materials. Each chapter begins with a brief overview and concludes with a range of exercises at varying levels of difficulty. Proofs are spelled out in detail. The volume includes a discussion of self-similar processes, ARMA, and fractional ARIMA time series with stable innovations. Annotation copyright by Book News, Inc., Portland, OR
Stable Non-Gaussian Random Processes
Title | Stable Non-Gaussian Random Processes PDF eBook |
Author | Gennady Samoradnitsky |
Publisher | Routledge |
Pages | 662 |
Release | 2017-11-22 |
Genre | Mathematics |
ISBN | 1351414798 |
This book serves as a standard reference, making this area accessible not only to researchers in probability and statistics, but also to graduate students and practitioners. The book assumes only a first-year graduate course in probability. Each chapter begins with a brief overview and concludes with a wide range of exercises at varying levels of difficulty. The authors supply detailed hints for the more challenging problems, and cover many advances made in recent years.
Stable Non-Gaussian Self-Similar Processes with Stationary Increments
Title | Stable Non-Gaussian Self-Similar Processes with Stationary Increments PDF eBook |
Author | Vladas Pipiras |
Publisher | Springer |
Pages | 143 |
Release | 2017-08-31 |
Genre | Mathematics |
ISBN | 3319623311 |
This book provides a self-contained presentation on the structure of a large class of stable processes, known as self-similar mixed moving averages. The authors present a way to describe and classify these processes by relating them to so-called deterministic flows. The first sections in the book review random variables, stochastic processes, and integrals, moving on to rigidity and flows, and finally ending with mixed moving averages and self-similarity. In-depth appendices are also included. This book is aimed at graduate students and researchers working in probability theory and statistics.
Random Processes by Example
Title | Random Processes by Example PDF eBook |
Author | Mikhail Lifshits |
Publisher | World Scientific |
Pages | 232 |
Release | 2014 |
Genre | Mathematics |
ISBN | 9814522295 |
This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable distributions and processes. Next, it illustrates general concepts by handling a transparent but rich example of a OC teletraffic modelOCO. A minor tuning of a few parameters of the model leads to different workload regimes, including Wiener process, fractional Brownian motion and stable L(r)vy process. The simplicity of the dependence mechanism used in the model enables us to get a clear understanding of long and short range dependence phenomena. The model also shows how light or heavy distribution tails lead to continuous Gaussian processes or to processes with jumps in the limiting regime. Finally, in this volume, readers will find discussions on the multivariate extensions that admit a variety of completely different applied interpretations. The reader will quickly become familiar with key concepts that form a language for many major probabilistic models of real world phenomena but are often neglected in more traditional courses of stochastic processes. Sample Chapter(s). Chapter 1: Preliminaries (367 KB). Contents: Preliminaries: Random Variables: A Summary; From Poisson to Stable Variables; Limit Theorems for Sums and Domains of Attraction; Random Vectors; Random Processes: Random Processes: Main Classes; Examples of Gaussian Random Processes; Random Measures and Stochastic Integrals; Limit Theorems for Poisson Integrals; L(r)vy Processes; Spectral Representations; Convergence of Random Processes; Teletraffic Models: A Model of Service System; Limit Theorems for the Workload; Micropulse Model; Spacial Extensions. Readership: Graduate students and researchers in probability & statist
Techniques for Treating Non-Gaussian Random Processes
Title | Techniques for Treating Non-Gaussian Random Processes PDF eBook |
Author | Robert Jay Hermann |
Publisher | |
Pages | 138 |
Release | 1963 |
Genre | Electric filters |
ISBN |
Applied Non-Gaussian Processes
Title | Applied Non-Gaussian Processes PDF eBook |
Author | Mircea Grigoriu |
Publisher | Prentice Hall |
Pages | 472 |
Release | 1995 |
Genre | Computers |
ISBN |
This text defines a variety of non-Gaussian processes, develops methods for generating realizations of non-Gaussian models, and provides methods for finding probabilistic characteristics of the output of linear filters with non-Gaussian inputs.
Introduction to Random Processes
Title | Introduction to Random Processes PDF eBook |
Author | E. Wong |
Publisher | Springer Science & Business Media |
Pages | 183 |
Release | 2013-03-09 |
Genre | Mathematics |
ISBN | 1475717954 |