Weak Dependence With Examples and Applications, Paperback by Dedecker, Jerome...-image

Weak Dependence With Examples and Applications, Paperback by Dedecker, Jerome...

$ 84.56

Publication Name: Weak Dependence : with Examples and Applications Publisher: Springer New York Subject Area: Mathematics Subject: Probability & Statistics / Stochastic Processes, Probability & Statistics / General, Algebra / General Number of Pages: Xiv, 322 Pages Language: English Item Length: 9.3 in Item Weight: 18.2 Oz Type: Textbook width: 6.1 in height: 0.2 in Series: Lecture Notes in Statistics Ser. Publication Year: 2007 ISBN: 9780387699516 Book Title: Weak Dependence With Examples and Applications Format: Perfect Item Width: 6.1 in Author: Sana Louhichi, Paul Doukhan, José Rafael León, Jérôme Dedecker, Gabriel Lang Item Height: 0.2 in

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Weak Dependence With Examples and Applications, Paperback by Dedecker, Jerome.... Weak Dependence With Examples and Applications, Paperback by Dedecker, Jerome; Doukhan, Paul; Lang, Gabriel; Leon, Jose Rafael; Louhichi, Sana, ISBN 0387699511, ISBN-13 9780387699516, Brand New, Free shipping in the US 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. Weak Dependence With Examples and Applications, Paperback by Dedecker, Jerome; Doukhan, Paul; Lang, Gabriel; Leon, Jose Rafael; Louhichi, Sana, ISBN 0387699511, ISBN-13 9780387699516, Brand New, Free shipping in the US 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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