A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single variable fraction Brownian motion (fBm) that was corrupted by additive noise. The procedure involve the construction of signal plus noise model and from the model we derive the likelihood function of the signal plus noise. We then estimate the fractal dimension by maximizing the likelihood function. The MLE was tested on fractal signal plus noise and the result was compared with other fractal dimension estimators such as the Box method which does not handle additive noise. The results shows that the MLE gives better results than the Box metho
The multifractal spectrum characterizes the scaling and singularity structures of signals and proves...
AbstractFractal Gaussian models have been widely used to represent the singular behavior of phenomen...
It is well known that the time series behaviour of non-linear systems with fractal attractors has a ...
A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single var...
A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single var...
A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single var...
The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelih...
The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelih...
The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelih...
Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of thi...
Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of thi...
Caption title.Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Advanced ...
In this paper we estimate the fractal dimension of stochastic processes with 1/f-like spectra by app...
We consider Bayesian inference via Markov chain Monte Carlo for a variety of fractal Gaussian proces...
We consider Bayesian inference via Markov chain Monte Carlo for a variety of fractal Gaussian proces...
The multifractal spectrum characterizes the scaling and singularity structures of signals and proves...
AbstractFractal Gaussian models have been widely used to represent the singular behavior of phenomen...
It is well known that the time series behaviour of non-linear systems with fractal attractors has a ...
A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single var...
A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single var...
A maximum likelihood estimator (MLE) is developed for estimating the fractal dimension of single var...
The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelih...
The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelih...
The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelih...
Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of thi...
Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of thi...
Caption title.Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Advanced ...
In this paper we estimate the fractal dimension of stochastic processes with 1/f-like spectra by app...
We consider Bayesian inference via Markov chain Monte Carlo for a variety of fractal Gaussian proces...
We consider Bayesian inference via Markov chain Monte Carlo for a variety of fractal Gaussian proces...
The multifractal spectrum characterizes the scaling and singularity structures of signals and proves...
AbstractFractal Gaussian models have been widely used to represent the singular behavior of phenomen...
It is well known that the time series behaviour of non-linear systems with fractal attractors has a ...