Skip to main content
SpringerLink
Log in

Robust and Nonlinear Time Series Analysis

Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 “Stochastische Mathematische Modelle”, Heidelberg 1983

  • Conference proceedings
  • © 1984

Overview

Editors:
  1. Jürgen Franke

    1. Fachbereich Mathematik, Universität Frankfurt, Frankfurt, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Wolfgang Härdle

    1. Fachbereich Mathematik, Universität Frankfurt, Frankfurt, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Douglas Martin

    1. Department of Statistics, GN-22, University of Washington, Seattle, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Statistics (LNS, volume 26)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (16 papers)

  1. Front Matter

    Pages N2-IX
    Download chapter PDF

  2. On the Use of Bayesian Models in Time Series Analysis

    • Hirotugu Akaike
    Pages 1-16

  3. Order Determination for Processes with Infinite Variance

    • R. J. Bhansali
    Pages 17-25

  4. Asymptotic Behaviour of the Estimates Based on Residual Autocovariances for ARMA Models

    • Oscar Bustos, Ricardo Fraiman, Victor J. Yohai
    Pages 26-49

  5. Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks

    • Rainer Dahlhaus
    Pages 50-67

  6. Linear Error-in-Variables Models

    • M. Deistler
    Pages 68-86

  7. Minimax-Robust Filtering and Finite-Length Robust Predictors

    • Jürgen Franke, H. Vincent Poor
    Pages 87-126

  8. The Problem of Unsuspected Serial Correlations

    • H. Graf, F. R. Hampel, J.-D. Tacier
    Pages 127-145

  9. The Estimation of ARMA Processes

    • E. J. Hannan
    Pages 146-162

  10. How to determine the bandwidth of some nonlinear smoothers in practice

    • Wolfgang Härdle
    Pages 163-184

  11. Remarks on Nongaussian Linear Processes with Additive Gaussian Noise

    • K. S. Lii, M. Rosenblatt
    Pages 185-197

  12. Gross-Error Sensitivies of GM and RA-Estimates

    • R. Douglas Martin, V. J. Yohai
    Pages 198-217

  13. Some Aspects of Qualitative Robustness in Time Series

    • P. Papantoni-Kazakos
    Pages 218-230

  14. Tightness of the Sequence of Empiric C.D.F. Processes Defined from Regression Fractiles

    • Stephen Portnoy
    Pages 231-246

  15. Robust Nonparametric Autoregression

    • P. M. Robinson
    Pages 247-255

  16. Robust Regression by Means of S-Estimators

    • P. Rousseeuw, V. Yohai
    Pages 256-272

  17. On Robust Estimation of Parameters for Autoregressive Moving Average Models

    • Pham Dinh Tuan
    Pages 273-286

  18. Back Matter

    Pages 287-287
    Download chapter PDF
Back to top

Keywords

About this book

Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be­ cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica­ tion. They have none-the-less stressed the importance of such optimali­ ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum­ ed model.

Editors and Affiliations

  • Fachbereich Mathematik, Universität Frankfurt, Frankfurt, Germany

    Jürgen Franke, Wolfgang Härdle

  • Department of Statistics, GN-22, University of Washington, Seattle, USA

    Douglas Martin

Bibliographic Information

  • Book Title: Robust and Nonlinear Time Series Analysis

  • Book Subtitle: Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 “Stochastische Mathematische Modelle”, Heidelberg 1983

  • Editors: Jürgen Franke, Wolfgang Härdle, Douglas Martin

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4615-7821-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1984

  • Softcover ISBN: 978-0-387-96102-6Published: 03 December 1984

  • eBook ISBN: 978-1-4615-7821-5Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: 286

  • Topics: Probability Theory and Stochastic Processes, Applications of Mathematics

Publish with us

Policies and ethics

Back to top

Search

Navigation