Lecture Notes in Statistics

Robust and Nonlinear Time Series Analysis

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

Editors: Franke, J., Härdle, W., Martin, D. (Eds.)

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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.

Table of contents (16 chapters)

  • On the Use of Bayesian Models in Time Series Analysis

    Akaike, Hirotugu

    Pages 1-16

  • Order Determination for Processes with Infinite Variance

    Bhansali, R. J.

    Pages 17-25

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

    Bustos, Oscar (et al.)

    Pages 26-49

  • Parameter Estimation of Stationary Processes with Spectra Containing Strong Peaks

    Dahlhaus, Rainer

    Pages 50-67

  • Linear Error-in-Variables Models

    Deistler, M.

    Pages 68-86

Buy this book

eBook $109.00
price for USA (gross)
  • ISBN 978-1-4615-7821-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $139.00
price for USA
  • ISBN 978-0-387-96102-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

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
  • J. Franke
  • W. Härdle
  • D. Martin
Series Title
Lecture Notes in Statistics
Series Volume
26
Copyright
1984
Publisher
Springer-Verlag New York
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-1-4615-7821-5
DOI
10.1007/978-1-4615-7821-5
Softcover ISBN
978-0-387-96102-6
Series ISSN
0930-0325
Edition Number
1
Number of Pages
286
Topics