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Dependence in Probability and Statistics

  • Book
  • © 2010

Overview

Editors:
  1. Paul Doukhan

    1. UFR Sciences-Techniques, Pontoise, France

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  2. Gabriel Lang

    1. , UMR MIA 518, INRA AgroParisTech, Paris, France

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    You can also search for this editor in PubMed   Google Scholar

  3. Donatas Surgailis

    1. Stochastic Processes Department, Vilnius, Lithuania

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  4. Gilles Teyssière

    1. Aarhus University, School of Economics, CREATES, Aarhus C, Denmark

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  • This volume provides the reader with a comprehensive recent account on dependent stochastic processes
  • This book is a reference book for theoretical works, and provides some results that are of straight practical interest for the applied statistician/econometrician
  • Includes supplementary material: sn.pub/extras

Part of the book series: Lecture Notes in Statistics (LNS, volume 200)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xv
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  2. Permutation and bootstrap statistics under infinite variance

    • István Berkes, Lajos Horváth, Johannes Schauer
    Pages 1-20

  3. Max–Stable Processes: Representations, Ergodic Properties and Statistical Applications

    • Stilian A. Stoev
    Pages 21-42

  4. Best attainable rates of convergence for the estimation of the memory parameter

    • Philippe Soulier
    Pages 43-57

  5. Harmonic analysis tools for statistical inference in the spectral domain

    • Florin Avram, Nikolai Leonenko, Ludmila Sakhno
    Pages 59-70

  6. On the impact of the number of vanishing moments on the dependence structures of compound Poisson motion and fractional Brownian motion in multifractal time

    • Béatrice Vedel, Herwig Wendt, Patrice Abry, Stéphane Jaffard
    Pages 71-101

  7. Multifractal scenarios for products of geometric Ornstein-Uhlenbeck type processes

    • Vo V. Anh, Nikolai N. Leonenko, Narn-Rueih Shieh
    Pages 103-122

  8. A new look at measuring dependence

    • Wei Biao Wu, Jan Mielniczuk
    Pages 123-142

  9. Robust regression with infinite moving average errors

    • Patrick J. Farrell, Mohamedou Ould-Haye
    Pages 143-157

  10. A note on the monitoring of changes in linear models with dependent errors

    • Alexander Schmitz, Josef G. Steinebach
    Pages 159-174

  11. Testing for homogeneity of variance in the wavelet domain.

    • Olaf Kouamo, Eric Moulines, Francois Roueff
    Pages 175-205

  12. Back Matter

    Pages 206-208
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Keywords

About this book

This volume collects recent works on weakly dependent, long-memory and multifractal processes and introduces new dependence measures for studying complex stochastic systems. Other topics include the statistical theory for bootstrap and permutation statistics for infinite variance processes, the dependence structure of max-stable processes, and the statistical properties of spectral estimators of the long memory parameter. The asymptotic behavior of Fejér graph integrals and their use for proving central limit theorems for tapered estimators are investigated. New multifractal processes are introduced and their multifractal properties analyzed. Wavelet-based methods are used to study multifractal processes with different multiresolution quantities, and to detect changes in the variance of random processes. Linear regression models with long-range dependent errors are studied, as is the issue of detecting changes in their parameters.

Editors and Affiliations

  • UFR Sciences-Techniques, Pontoise, France

    Paul Doukhan

  • , UMR MIA 518, INRA AgroParisTech, Paris, France

    Gabriel Lang

  • Stochastic Processes Department, Vilnius, Lithuania

    Donatas Surgailis

  • Aarhus University, School of Economics, CREATES, Aarhus C, Denmark

    Gilles Teyssière

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