Buch, Englisch, Band 190, 322 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 511 g
Reihe: Lecture Notes in Statistics
Buch, Englisch, Band 190, 322 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 511 g
Reihe: Lecture Notes in Statistics
ISBN: 978-0-387-69951-6
Verlag: Springer
This monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measure asymptotic independence of a random process. The authors propose various examples of models fitting such conditions such as stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Most of the commonly used stationary models fit their conditions. The simplicity of the conditions is also their strength. The main tools for an asymptotic theory are developed under weak dependence. They apply the theory to nonparametric statistics, spectral analysis, econometrics, and resampling. The level of generality makes those techniques quite robust with respect to the model. The limit theorems are sometimes sharp and always simple to apply. The theory (with proofs) is developed and the authors propose to fix the notation for future applications. Several applications are still needed to develop a method of analysis for (nonlinear) times series and they provide here a strong basis for such studies.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Weak dependence.- Models.- Tools for non causal cases.- Tools for causal cases.- Applications of strong laws of large numbers.- Central Limit theorem.- Donsker Principles.- Law of the iterated logarithm (LIL).- The Empirical process.- Functional estimation.- Spectral estimation.- Econometric applications and resampling.
