Stable Non-Gaussian Random Processes - Stochastic Models with Infinite Variance (Hardcover)

The familiar Gaussian models do not allow for large deviations and are thus often inadequate for modeling high variability. Non-Gaussian stable models do not possess such limitations. They all share a familiar feature which differentiates them from the Gaussian ones. Their marginal distributions possess heavy "probability tails", always with infinite variance and in some cases with infinite first moment. The aim of this book is to make this exciting material easily accessible to graduate students and practitioners. Assuming only a first-year graduate course in probability, it includes material which has appeared only recently in journals and unpublished materials.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 book includes a discussion of self-similar processes, ARMA, and fractional ARIMA time series with stable innovations.

General

Imprint:	Chapman & Hall/CRC
Country of origin:	United States
Series:	Stochastic Modeling Series, 1
Release date:	June 1994
First published:	1994
Authors:	Gennady Samoradnitsky • M.S. Taqqu
Dimensions:	234 x 156 x 45mm (L x W x T)
Format:	Hardcover
Pages:	632
ISBN-13:	978-0-412-05171-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Science & Mathematics > Mathematics > Applied mathematics > Stochastics
LSN:	0-412-05171-0
Barcode:	9780412051715

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