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Empirical Process Techniques for Dependent Data (Paperback, Softcover reprint of the original 1st ed. 2002)
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Empirical Process Techniques for Dependent Data (Paperback, Softcover reprint of the original 1st ed. 2002)
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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,
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