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Nonparametric Estimation of a Class of Nonlinear Time Series Models

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Nonparametric Functional Estimation and Related Topics

Part of the book series: NATO ASI Series ((ASIC,volume 335))

Abstract

The problem of estimation of nonlinear time series models which are a composition of nonlinear elements and linear stochastic processes is considered. The compositions studied include the cascade and parallel connections. The problem of nonparametric estimation of underlying nonlinearities is examined. It is resolved by solving Fredholm’s integral equations of the second kind arising in the estimation problem. As a result, the nonparametric orthogonal series estimates of nonlinearities are derived and their asymptotic as well as some small sample properties are established.

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References

  1. Andrews, D.W.K. (1984), ‘Non-strong mixing autoregressive processes’, J. Appl. Prob., 21, 930–934.

    Article  MATH  Google Scholar 

  2. Andrews, D.W.K. (1985), ‘A nearly independent, but non-strong mixing, triangular array’, J. Appl. Prob., 22, 729–731.

    Article  MATH  Google Scholar 

  3. Bierens, H.T. (1983), ‘Uniform consistency of kernel estimators of a regression function under generalized conditions’, J. Amer. Statist. Assoc., 78, 699–707.

    Article  MathSciNet  MATH  Google Scholar 

  4. Breiman, L. and Friedman, J.H. (1985), ‘Estimating optimal transformations for multiple regression and correlation’, J. Amer. Statist. Assoc., 80, 580–597.

    Article  MathSciNet  MATH  Google Scholar 

  5. Brillinger, D.R. (1977), ‘The identification of a particular nonlinear timer series system’, Biometrika, 64, 509–515.

    Article  MathSciNet  MATH  Google Scholar 

  6. Brillinger, D.R. (1980), Time Series. Data Analysis and Theory, Holden-Day, Inc., San Francisco, Expanded Edition.

    MATH  Google Scholar 

  7. Collomb, G. and Härdle, W. (1985), ‘Strong uniform convergence rates in robust nonparametric time series analysis and prediction: kernel regression estimation from dependent observations’, Stochastic Process.Appl., 23, 77–81.

    Article  Google Scholar 

  8. Eubank, R.L. (1980), Spline Smoothing and Nonparametric Regression, Marcel Dekker, New York.

    Google Scholar 

  9. Georgiev, A. (1984), ‘Nonparametric system identification by kernel methods’, IEEE Trans. Automat. Control, AC-29, 356–358.

    Article  Google Scholar 

  10. Greblicki, W. and Pawlak, M. (1986), ‘Identification of discrete Hammerstein systems using kernel regression estimates’, IEEE Trans. Automat. Control, AC-31, 74–77.

    Article  Google Scholar 

  11. Greblicki, W. and Pawlak, M. (1989), ‘Recursive nonparametric identification of Hammerstein systems’, J. of Franklin Institute, 326, 461–481.

    Article  MathSciNet  MATH  Google Scholar 

  12. Greblicki, W. and Pawlak, M. (1989), ‘Nonparametric identification of Hammerstein systems’, IEEE Trans. Inform. Theory, 35, 409–418.

    Article  MathSciNet  MATH  Google Scholar 

  13. Greblicki, W. and Pawlak, M. (1984), ‘Hermite series estimates of a probability density and its derivatives’, J. Multivariate Anal. 15, 176–182.

    Article  MathSciNet  Google Scholar 

  14. Greblicki, W. and Pawlak, M. (1985), ‘Fourier and Hermite series estimates of regression functions’, Ann. Inst. Statist. Math., 37, 443–459.

    Article  MathSciNet  MATH  Google Scholar 

  15. Györfi, L., Härdle, W., Sarda, P. and Vieu, P. (1989), Nonparametric Curve Estimation from Time Series. Springer-Verlag, Berlin.

    MATH  Google Scholar 

  16. Hall, P. (1987), ‘Cross-validation and the smoothing of orthogonal series density estimators’, J. Multivariate Anal., 21, 189–206.

    Article  MathSciNet  MATH  Google Scholar 

  17. Hunter, I.W. and Korenberg, M.J. (1986), ‘The identification of nonlinear biological systems: Wiener and Hammerstein cascade models’, Biol. Cybern., 55, 135–144.

    MathSciNet  MATH  Google Scholar 

  18. Krzyzak, A. (1990), ‘On estimation of a class of nonlinear systems by the kernel regression estimate’, IEEE Trans. Inform. Theory, IT-36, 141–152.

    Article  MathSciNet  Google Scholar 

  19. Lancaster, H.O. (1958), ‘The structure of bivariate distributions’, Ann. Math. Statist., 29, 719–736.

    Article  MathSciNet  MATH  Google Scholar 

  20. O’Leary, D.P. (1985), ‘Identification of sensory systems and neural systems’, IFAC Symposium on Identification and System Parameter Estimation, Pergamon Press, 77–84.

    Google Scholar 

  21. Pawlak, M. (1988), ‘Identification of a class of parallel systems’, The 26th Allerton Conf. in Communication, Control, and Computing, I, 366–367.

    Google Scholar 

  22. Pawlak, M. (1990), ‘Estimation of a class of additive nonlinear time series models’, Manuscript in preparation.

    Google Scholar 

  23. Robinson, P.M. (1986), ‘On the consistency and finite-sample properties on nonparametric kernel time series regression, autoregression and density estimators’, Ann. Inst. Statist. Math., Part A, 38, 539–549.

    Article  MATH  Google Scholar 

  24. Stone, C.T. (1985), ‘Additive regression and other nonparametric models’, Ann. Statist., 13, 689–705.

    Article  MathSciNet  MATH  Google Scholar 

  25. Stone, C.J. (1977), ‘Consistent nonparametric regression’, Ann. Statist., 5, 595–645.

    Article  MathSciNet  MATH  Google Scholar 

  26. Wise, G.L. and Thomas, J.B. (1975), ‘A characterization of Markov sequences’, J. Franklin Institute, 229, 269–278.

    Article  MathSciNet  Google Scholar 

  27. Yakowitz, S. (1985), ‘Nonparametric density estimation prediction and regression for Markov sequences’, J. Amer. Statist. Assoc., 80, 215–221.

    Article  MathSciNet  MATH  Google Scholar 

  28. Tricomi, F.G. (1985), Integral Equations, Dover, New York.

    Google Scholar 

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© 1991 Springer Science+Business Media Dordrecht

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Pawlak, M., Greblicki, W. (1991). Nonparametric Estimation of a Class of Nonlinear Time Series Models. In: Roussas, G. (eds) Nonparametric Functional Estimation and Related Topics. NATO ASI Series, vol 335. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3222-0_40

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  • DOI: https://doi.org/10.1007/978-94-011-3222-0_40

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-5420-1

  • Online ISBN: 978-94-011-3222-0

  • eBook Packages: Springer Book Archive

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