Dependence in Probability and Statistics
This volume collects recent works on weakly dependent, long-memory and multifractal processes and introduces new dependence measures for studying complex stochastic systems. Other topics include the statistical theory for bootstrap and permutation statist
- PDF / 3,854,674 Bytes
- 221 Pages / 439.37 x 666.142 pts Page_size
- 31 Downloads / 242 Views
200
Paul Doukhan • Gabriel Lang • Donatas Surgailis • Gilles Teyssière Editors
Dependence in Probability and Statistics
ABC
Editors Prof. Paul Doukhan UFR Sciences-Techniques avenue Adolphe-Chauvin 2 95302 Pontoise France [email protected] Prof. Dr. Donatas Surgailis Stochastic Processes Department Akademijos str. 4 08412 Vilnius Lithuania [email protected]
Prof. Dr. Gabriel Lang INRA AgroParisTech UMR MIA 518 75005 Paris France [email protected] Dr. Gilles Teyssière CREATES School of Economics Aarhus University Bartholins Allé 10 8000 Aarhus C Denmark [email protected]
ISSN 0930-0325 ISBN 978-3-642-14103-4 e-ISBN 978-3-642-14104-1 DOI 10.1007/978-3-642-14104-1 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2010931866 © Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: SPi Publisher Services Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This volume contains several contributions on the general theme of dependence for several classes of stochastic processes, and its implications on asymptotic properties of various statistics and on statistical inference issues in statistics and econometrics. The chapter by Berkes, Horváth and Schauer is a survey on their recent results on bootstrap and permutation statistics when the negligibility condition of classical central limit theory is not satisfied. These results are of interest for describing the asymptotic properties of bootstrap and permutation statistics in case of infinite variances, and for applications to statistical inference, e.g., the change-point problem. The paper by Stoev reviews some recent results by the author on ergodicity of max-stable processes. Max-stable processes play a central role in the modeling of extreme value phenomena and appear as limits of component-wise maxima. At the present time, a rather complete and interesting picture of the dependence structure of max-stable processes has emerged, involving spectral functions, extremal stochastic integrals, mixed moving maxima, and other analytic and probabilistic tools. For statistical applications, the problem of ergodicity or no
Data Loading...
