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Periodogram estimates in nonlinear regression models with long-range dependent noise

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Cybernetics and Systems Analysis Aims and scope

Abstract

We propose a method to identify the parameters of the “signal plus noise” nonlinear regression model. We investigate the periodogram estimates and prove their strong consistency provided that the noise is a functional of a stationary Gaussian process with long-range dependence and the regression function is almost periodic.

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Authors and Affiliations

  1. V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine

    P. S. Knopov & G. D. Bila

Authors
  1. P. S. Knopov
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  2. G. D. Bila
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Correspondence to P. S. Knopov.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 4, July–August, 2013, pp. 163–172.

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Knopov, P.S., Bila, G.D. Periodogram estimates in nonlinear regression models with long-range dependent noise. Cybern Syst Anal 49, 624–631 (2013). https://doi.org/10.1007/s10559-013-9549-5

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  • DOI: https://doi.org/10.1007/s10559-013-9549-5

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