Könyv Stable Processes and Related Topics ambanis

Stable Processes and Related Topics

A Selection of Papers from the Mathematical Sciences Institute Workshop, January 9-13, 1990

Szerző: ambanis
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Springer, Basel
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
19 900 Ft
The Workshop on Stable Processes and Related Topics took place at Cor nell University in January 9-1...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
1991
oldal
330
EAN
9781468467802
ISBN
1468467808
Enbook ID
02232880
Súly
499
Méretek
152 x 229 x 18

Teljes leírás

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.

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