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Nonparametric Methods for Time Series and Dynamical Systems

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Statistical Challenges in Modern Astronomy II

Abstract

We present different approaches to reconstruct a chaotic map and to identify existence of chaos. Using nonparametric techniques, we construct - from observational data - estimates of the embedding dimension, the chaotic map, the invariant measure and the Lyapunov exponent.

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Author information

Authors and Affiliations

  1. ENSAE-CREST, UA742, Timbre J120, 3 av. Pierre Larousse, 92245, Malakoff Cedex, France

    D. Guégan

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  1. D. Guégan
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Editor information

Editors and Affiliations

  1. Department of Statistics, Pennsylvania State University, University Park, PA, 16802, USA

    G. Jogesh Babu

  2. Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA, 16802, USA

    Eric D. Feigelson

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© 1997 Springer Science+Business Media New York

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Guégan, D. (1997). Nonparametric Methods for Time Series and Dynamical Systems. In: Babu, G.J., Feigelson, E.D. (eds) Statistical Challenges in Modern Astronomy II. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1968-2_17

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  • DOI: https://doi.org/10.1007/978-1-4612-1968-2_17

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7360-8

  • Online ISBN: 978-1-4612-1968-2

  • eBook Packages: Springer Book Archive

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