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.<>
Parameter estimation of a fractional Brownian motion in a white noise using wavelets
1994-01-01
283361 byte
Conference paper
Electronic Resource
English
Parameter Estimation of a Fractional Brownian Motion in a White Noise Using Wavelets
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