Empirical Process Techniques for Dependent Data 2002 Edition Contributor(s): Dehling, Herold (Editor), Mikosch, Thomas (Editor), Sörensen, Michael (Editor) |
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ISBN: 0817642013 ISBN-13: 9780817642013 Publisher: Birkhauser OUR PRICE: $151.99 Product Type: Hardcover Published: August 2002 Annotation: This book contains accessible surveys by several leading experts in the field. The first part is a thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data, starting from the classical contributions of Billingsley and including present day research. The bibliography provides an excellent basis for further studies. The remaining parts of the book give an overview of the most recent applications in various fields related to empirical processes such as 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. This book 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, analysis, extreme value theory, point process theory, and applied probability theory. |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - General - Business & Economics | Statistics |
Dewey: 519.5 |
LCCN: 2002071106 |
Physical Information: 0.94" H x 7.06" W x 10.14" (1.99 lbs) 383 pages |
Descriptions, Reviews, Etc. |
Publisher Description: This book contains accessible surveys by several leading experts in the field. The first part is a thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data, starting from the classical contributions of Billingsley and including present day research. The bibliography provides an excellent basis for further studies. The remaining parts of the book give an overview of the most recent applications in various fields related to empirical processes such as 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. |