On nonparametric kernel identification of nonlinear autoregression process

A piecewise-smoothed approximation for nonparametric regression estimation is proposed. The mean square convergence of this approximation from a dependent sample satisfying strong mixing conditions is proved. The main part of the asymptotic mean square error for the proposed modification of the kern...

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Published in5th Korea-Russia International Symposium on Science and Technology. Proceedings. KORUS 2001 (Cat. No.01EX478) Vol. 2; pp. 208 - 211 vol.2
Main Authors Kitaeva, A.V., Koshkin, G.M., Piven, I.G., Ryumkin, V.I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Convergence
Covariance matrix
Kernel
Mathematics
Mean square error methods
Random variables
State estimation
Statistics
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Abstract A piecewise-smoothed approximation for nonparametric regression estimation is proposed. The mean square convergence of this approximation from a dependent sample satisfying strong mixing conditions is proved. The main part of the asymptotic mean square error for the proposed modification of the kernel regression estimate is found. These results are used for the identification of the nonlinear autoregression process.
AbstractList A piecewise-smoothed approximation for nonparametric regression estimation is proposed. The mean square convergence of this approximation from a dependent sample satisfying strong mixing conditions is proved. The main part of the asymptotic mean square error for the proposed modification of the kernel regression estimate is found. These results are used for the identification of the nonlinear autoregression process.
Author Koshkin, G.M.
Piven, I.G.
Kitaeva, A.V.
Ryumkin, V.I.
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  givenname: V.I.
  surname: Ryumkin
  fullname: Ryumkin, V.I.
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Snippet A piecewise-smoothed approximation for nonparametric regression estimation is proposed. The mean square convergence of this approximation from a dependent...
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StartPage 208
SubjectTerms Convergence
Covariance matrix
Kernel
Mathematics
Mean square error methods
Random variables
State estimation
Statistics
Title On nonparametric kernel identification of nonlinear autoregression process
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