A novel pseudolinear method for the estimation of fractionally integrated ARMA (ARFIMA) models that are capable of representing combined long- and short-term dependency, is introduced. The method is based on the relationship of the AR/MA parameters and the coefficients of the fractional power operator binomial series expansion with the model's inverse function. These lead to the formulation of a special-form regression problem that can be decomposed into a univariate nonlinear and a multivariate linear regression and may be thus tackled via a special pseudolinear procedure. This decomposition in turn leads to the elimination of the need for initial guess parameter values, drastic simplification in the detection and handling of potential local extrema problems, as well as computational simplicity. The method's strong consistency is established, whereas its performance characteristics are assessed via Monte Carlo experiments and comparisons with the maximum likelihood method. The pseudolinear method is also used for the ARFIMA modeling and prediction of power consumption in an experimental automobile fully active suspension system, the consumption of which is shown to exhibit long-term dependency. A comparison with ARMA/ARIMA type modeling is also made, and the obtained ARFIMA models are shown to achieve improved predictive performance at a drastically reduced parametric complexity.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Pseudolinear estimation of fractionally integrated ARMA (ARFIMA) models with automotive application


    Contributors:

    Published in:

    Publication date :

    1999


    Size :

    16 Seiten, 26 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




    Neural Control of Space Trajectories with Pseudolinear Models

    Belizário, Pedro M. C. / Bousson, K. / Senra, Filipe | Springer Verlag | 2023


    Biased Estimation Properties of the Pseudolinear Tracking Filter

    Aidala, Vincent J. / Nardone, Steven C. | IEEE | 1982



    Nonlinear Shannon Limit in Pseudolinear Coherent Systems

    Mecozzi, A. / Essiambre, R.-J. | British Library Online Contents | 2012