PROGRESS IN PROBABILITY: STABLE PROCESSES AND RELATED TOPICS

EDS STAMATIS CAMBANIS; GENNADY SAMORODNITSKY; MURAD S. TAQQU

ISBN 10: 0817634851 / ISBN 13: 9780817634858

Published by Birkhäuser, 1991

About this Item

BOOK IS IN VG/NF CONDITION. Looks brand new. Points are sharp and spine is square and intact. Pages are white with light discoloration on bottom from the shelf. There is no visible wear on the covers or binding of this book. Please see photos attached and feel free to reach out with questions about this title.

Seller Inventory # ABE-1763421132292

Bibliographic Details

• Title: PROGRESS IN PROBABILITY: STABLE PROCESSES AND RELATED TOPICS
• Publisher: Birkhäuser
• Publication Date: 1991
• Language: English
• ISBN 10: 0817634851
• ISBN 13: 9780817634858
• Binding: Hardcover
• Edition: 1st Edition
• Condition: Fine
• Dust Jacket Condition: No Jacket
• Weight: 2 pounds

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.