This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.
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This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.
Imprint | Springer-Verlag New York |
Country of origin | United States |
Series | Lecture Notes in Statistics, 190 |
Release date | July 2007 |
Availability | Expected to ship within 10 - 15 working days |
First published | 2007 |
Authors | Jerome Dedecker, Paul Doukhan, Gabriel Lang, Jose Rafael Leon, Sana Louhichi, Clementine Prieur |
Dimensions | 235 x 155 x 17mm (L x W x T) |
Format | Paperback |
Pages | 322 |
Edition | 2007 ed. |
ISBN-13 | 978-0-387-69951-6 |
Barcode | 9780387699516 |
Categories | |
LSN | 0-387-69951-1 |