Introduction -- Weak dependence -- Models -- Tools for non causal cases -- Tools for causal cases -- Applications of SLLN -- Central limit theorem -- Donsker principles -- Law of the iterated logarithm (LIL) -- The empirical process -- Functional estimation -- Spectral estimation -- Econometrics and resampling
Summary
"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."--Jacket
Bibliography
Includes bibliographical references (pages 305-315) and index
