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Nonparametric semirecursive identification in a wide sense of strong mixing processes

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Abstract

We find principal parts of asymptotic mean-square errors of semirecursive nonparametric estimators of functionals of a multidimensional density function under the assumption that observations satisfy a strong mixing condition. Results are illustrated by an example of a nonlinear autoregression process.

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Authors and Affiliations

  1. Tomsk Polytechnical University, Tomsk, Russia

    A. V. Kitaeva

  2. Tomsk State University Department of Informatization Problems, Tomsk Scientific Center, Siberian Branch of the RAS, Tomsk, Russia

    G. M. Koshkin

Authors
  1. A. V. Kitaeva
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  2. G. M. Koshkin
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Correspondence to A. V. Kitaeva.

Additional information

Original Russian Text © A.V. Kitaeva, G.M. Koshkin, 2010, published in Problemy Peredachi Informatsii, 2010, Vol. 46, No. 1, pp. 25–41.

Supported in part by the Russian Foundation for Basic Research, project no. 09-08-00595a.

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Kitaeva, A.V., Koshkin, G.M. Nonparametric semirecursive identification in a wide sense of strong mixing processes. Probl Inf Transm 46, 22–37 (2010). https://doi.org/10.1134/S0032946010010047

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  • DOI: https://doi.org/10.1134/S0032946010010047

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