To discriminate the fractal parameter of a fractional Brownian motion (fBm) embedded in a white noise is equivalent to discriminating the composite singularity formed by superimposing a peak singularity upon a Dirac singularity. We use the autocorrelation of the wavelet transform coefficients to characterize the composite singularity, by formalizing this problem as a nonlinear optimization problem. We modify the internal penalty function method to efficiently estimate the parameters of the fBm in the white noise.<>


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    Titel :

    Parameter estimation of a fractional Brownian motion in a white noise using wavelets


    Beteiligte:


    Erscheinungsdatum :

    01.01.1994


    Format / Umfang :

    283361 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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