Advertisement

© 2013

Long-Memory Processes

Probabilistic Properties and Statistical Methods

Benefits

  • 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?

Book

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 1-41
  3. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 43-106
  4. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 107-208
  5. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 209-384
  6. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 385-528
  7. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 529-554
  8. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 555-732
  9. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 733-752
  10. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 753-769
  11. Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik
    Pages 771-795
  12. Back Matter
    Pages 797-884

About this book

Introduction

Long-memory processes are known to play an important part in many areas of science and technology, including physics, geophysics, hydrology, telecommunications, economics, finance, climatology, and network engineering. In the last 20 years enormous progress has been made in understanding the probabilistic foundations and statistical principles of such processes. This book provides a timely and comprehensive review, including a thorough discussion of mathematical and probabilistic foundations and statistical methods, emphasizing their practical motivation and mathematical justification. Proofs of the main theorems are provided and data examples illustrate practical aspects. This book will be a valuable resource for researchers and graduate students in statistics, mathematics, econometrics and other quantitative areas, as well as for practitioners and applied researchers who need to analyze data in which long memory, power laws, self-similar scaling or fractal properties are relevant.

Keywords

62Mxx, 62M09, 62M10, 60G18, 60G22, 60G52, 60G60, 91B84 asymptotic theory fractal processes long-memory processes statistical applications time series analysis/spatial processes/space-time processes

Authors and affiliations

  1. 1.Dept. of Mathematics and StatisticsUniversity of KonstanzKonstanzGermany
  2. 2.Faculty of Business, Administration and EconomicsUniversity of PaderbornPaderbornGermany
  3. 3.Swiss Federal Research Institute WSLBirmensdorfSwitzerland
  4. 4.Dept. of Mathematics and StatisticsUniversity of OttawaOttawaCanada

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

Industry Sectors
Health & Hospitals
Biotechnology
IT & Software
Telecommunications
Consumer Packaged Goods
Oil, Gas & Geosciences
Pharma
Materials & Steel
Finance, Business & Banking
Electronics
Energy, Utilities & Environment
Aerospace

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 who is working in fields related to long-memory processes with many update information and research topics.” (Weiping Li, zbMATH, Vol. 1282, 2014)