Overview
- Provides a comprehensive, in-depth and up-to-date review of available results in probability and statistical inference for long-memory and related processes
- Thoroughly addresses both theory and practice
- Proofs of the main theorems are provided and data examples are used to illustrate practical aspects
- Includes detailed discussions of a large variety of statistical and probabilistic techniques?
- Includes supplementary material: sn.pub/extras
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Reviews
From the book reviews:
“This encyclopaedic book covers almost the whole literature on univariate and multivariate long-range dependent (LRD) processes, or long-memory processes or strongly dependent processes. … This volume is then of strong interest for both researchers and teachers familiar with the topic, as it gives an overall, structured and balanced picture of the current state of the art. Readers less familiar with the topic will easily find their way in the vast literature on this issue, and will have their curiosity satisfied.” (Gilles Teyssière, Mathematical Reviews, October, 2014)
“This book aims to cover probabilistic and statistical aspects of long-memory processes in as much detail as possible, including a broad range of topics. The authors did an excellent job to reach their goals, and the book would be a must for researchers interested in long-memory processes and practioners on time series and data analysis. … the book is an excellent choice for anyone whois working in fields related to long-memory processes with many update information and research topics.” (Weiping Li, zbMATH, Vol. 1282, 2014)Authors and Affiliations
About the authors
Jan Beran is a Professor of Statistics at the University of Konstanz (Department of Mathematics and Statistics). After completing his PhD in Mathematics at the ETH Zurich, he worked at several U.S. universities and the University of Zurich. He has a broad range of interests, from long-memory processes and asymptotic theory to applications in finance, biology and musicology.
Yuanhua Feng is a Professor of Econometrics at the University of Paderborn’s Department of Economics. He previously worked at the Heriot-Watt University, UK, after completing his PhD and postdoctoral studies at the University of Konstanz. His research interests include financial econometrics, time series and semiparametric modeling.
Sucharita Ghosh (M.Stat. Indian Statistical Institute; PhD Univ. Toronto) is a statistician at the Swiss Federal Research Institute WSL. She has taught at the University of Toronto, UNC Chapel Hill, Cornell University, the University of Konstanz, University of York and the ETH Zurich. Her research interests include space-time processes, nonparametric curve estimation and empirical transforms.
Rafal Kulik is an Associate Professor at the University of Ottawa’s Department of Mathematics and Statistics. He has previously taught at the University of Wroclaw, University of Ulm and University of Sydney. His research interests include limit theorems for weakly and strongly dependent random variables, time series analysis and heavy-tailed phenomena, with applications in finance.
Bibliographic Information
Book Title: Long-Memory Processes
Book Subtitle: Probabilistic Properties and Statistical Methods
Authors: Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
DOI: https://doi.org/10.1007/978-3-642-35512-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-35511-0Published: 29 May 2013
Softcover ISBN: 978-3-662-51235-7Published: 23 August 2016
eBook ISBN: 978-3-642-35512-7Published: 14 May 2013
Edition Number: 1
Number of Pages: XVII, 884
Number of Illustrations: 29 b/w illustrations, 60 illustrations in colour
Topics: Statistical Theory and Methods, Probability Theory and Stochastic Processes, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Life Sciences, Medicine, Health Sciences