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Empirical process techniques for independent data have been used §for many years in statistics and probability theory. These techniques §have proved very useful for studying asymptotic properties of §parametric as well as non-parametric statistical procedures. Recently, §the need to model the dependence structure in data sets from many §different subject areas such as finance, insurance, and §telecommunications has led to new developments concerning the §empirical distribution function and the empirical process for §dependent, mostly stationary sequences. This work gives an §introduction to this new theory of empirical process techniques, which §has so far been scattered in the statistical and probabilistic §literature, and surveys the most recent developments in various §related fields. §Key features: A thorough and comprehensive introduction to the §existing theory of empirical process techniques for dependent data §Accessible surveys by leading experts of the most recent developments §in various related fields Examines empirical process techniques for §dependent data, useful for studying parametric and non-parametric §statistical procedures Comprehensive bibliographies An overview of §applications in various fields related to empirical processes: e.g., §spectral analysis of time-series, the bootstrap for stationary §sequences, extreme value theory, and the empirical process for mixing §dependent observations, including the case of strong dependence. §To date this book is the only comprehensive treatment of the topic §in book literature. It is an ideal introductory text that will serve §as a reference or resource for classroom use in the areas of §statistics, time-series analysis, extreme value theory, point process §theory, and applied probability theory. Contributors: P. Ango §Nze, M.A. Arcones, I. Berkes, R. Dahlhaus, J. Dedecker, H.G. Dehling,
