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Empirical Process Techniques for Dependent Data 2002 Edition
Contributor(s): Dehling, Herold (Editor), Mikosch, Thomas (Editor), Sörensen, Michael (Editor)
ISBN: 0817642013     ISBN-13: 9780817642013
Publisher: Birkhauser
OUR PRICE:   $151.99  
Product Type: Hardcover
Published: August 2002
Qty:
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.