Stable Processes and Related Topics

Cambanis, Stamatis, Gennady Samorodnitsky and Murad S. Taqqu, editors

ISBN 10: 0817634851 / ISBN 13: 9780817634858

Published by Birkhauser, Boston, 1991

About this Item

An ex-library copy in original green hard covers. The usual ex-libris markings. The binding is sound, the text is clean/unmarked, and there is little cover wear. No dust jacket, apparently as issued.

Seller Inventory # 064990

Bibliographic Details

• Title: Stable Processes and Related Topics
• Publisher: Birkhauser, Boston
• Publication Date: 1991
• Language: English
• ISBN 10: 0817634851
• ISBN 13: 9780817634858
• Binding: Hardcover
• Edition: First Edition.
• Condition: Good
• Dust Jacket Condition: No Dust Jacket
• Book Type: Book

About this title

Synopsis

The Workshop on Stable Processes and Related Topics took place at Cor­ nell University in January 9-13, 1990, under the sponsorship of the Mathemat­ ical Sciences Institute. It attracted an international roster of probabilists from Brazil, Japan, Korea, Poland, Germany, Holland and France as well as the U. S. This volume contains a sample of the papers presented at the Workshop. All the papers have been refereed. Gaussian processes have been studied extensively over the last fifty years and form the bedrock of stochastic modeling. Their importance stems from the Central Limit Theorem. They share a number of special properties which facilitates their analysis and makes them particularly suitable to statistical inference. The many properties they share, however, is also the seed of their limitations. What happens in the real world away from the ideal Gaussian model? The non-Gaussian world may contain random processes that are close to the Gaussian. What are appropriate classes of nearly Gaussian models and how typical or robust is the Gaussian model amongst them? Moving further away from normality, what are appropriate non-Gaussian models that are sufficiently different to encompass distinct behavior, yet sufficiently simple to be amenable to efficient statistical inference? The very Central Limit Theorem which provides the fundamental justifi­ cation for approximate normality, points to stable and other infinitely divisible models. Some of these may be close to and others very different from Gaussian models.