Asymptotic Nonparametric Statistical Analysis of Stationary Time Series
Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative
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Daniil Ryabko
Asymptotic Nonparametric Statistical Analysis of Stationary Time Series 123
SpringerBriefs in Computer Science Series editors Stan Zdonik, Brown University, Providence, RI, USA Shashi Shekhar, University of Minnesota, Minneapolis, MN, USA Xindong Wu, University of Vermont, Burlington, VT, USA Lakhmi C. Jain, University of South Australia, Adelaide, SA, Australia David Padua, University of Illinois Urbana-Champaign, Urbana, IL, USA Xuemin Sherman Shen, University of Waterloo, Waterloo, ON, Canada Borko Furht, Florida Atlantic University, Boca Raton, FL, USA V. S. Subrahmanian, Department of Computer Science, University of Maryland, College Park, MD, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, PA, USA Katsushi Ikeuchi, Meguro-ku, University of Tokyo, Tokyo, Japan Bruno Siciliano, Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, Napoli, Italy Sushil Jajodia, George Mason University, Fairfax, VA, USA Newton Lee, Institute for Education, Research and Scholarships, Los Angeles, CA, USA
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Daniil Ryabko
Asymptotic Nonparametric Statistical Analysis of Stationary Time Series
123
Daniil Ryabko Fishlife Research Belize City, Belize
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-3-030-12563-9 ISBN 978-3-030-12564-6 (eBook) https://doi.org/10.1007/978-3-030-12564-6 Library of Congress Control Number: 2019933098 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illus
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