An algorithm is proposed that allows to estimate the self-similarity parameter of a fractal k-dimensional stochastic process. Our technique greatly improves the processing times of a distribution-based estimator, that – introduced years ago – efficiently worked only in the one-dimensional distribution case
Statistical self-similarity of random processes in continuous-domains is defined through invariance ...
Introduction A stochastic process Y (t) is defined as self-similar with self-similarity parameter H...
A new method is proposed to estimate the self-similarity exponent. Instead of applying finite moment...
An algorithm is proposed that allows to estimate the self-similarity parameter of a fractal k-dimens...
Introduction A self-similar process is loosely defined as a stochastic process which generates a sa...
An estimator of the self-similarity parameter for certain classes of random processes is presented. ...
In a companion paper (see Self-Similarity: Part I—Splines and Operators), we characterized the class...
International audienceThis paper is devoted to the introduction of a new class of consistent estimat...
In the last years fractal models have become the focus of many contributions dealing with market dyn...
In this paper we estimate the fractal dimension of stochastic processes with 1/f-like spectra by app...
In studying the scale invariance of an empirical time series a twofold problem arises: it is necessa...
In studying the scale invariance of an empirical time series a twofold problem arises: it is necessa...
A stochastic process Y (t) is defined as self-similar with self-similarity parameter H if for any po...
This paper presents a generalized approach to the fractal analysis of self-similar random processes ...
Abstract-The fractional Brownian motion (fBm) model has proven to be valuable in modeling many natur...
Statistical self-similarity of random processes in continuous-domains is defined through invariance ...
Introduction A stochastic process Y (t) is defined as self-similar with self-similarity parameter H...
A new method is proposed to estimate the self-similarity exponent. Instead of applying finite moment...
An algorithm is proposed that allows to estimate the self-similarity parameter of a fractal k-dimens...
Introduction A self-similar process is loosely defined as a stochastic process which generates a sa...
An estimator of the self-similarity parameter for certain classes of random processes is presented. ...
In a companion paper (see Self-Similarity: Part I—Splines and Operators), we characterized the class...
International audienceThis paper is devoted to the introduction of a new class of consistent estimat...
In the last years fractal models have become the focus of many contributions dealing with market dyn...
In this paper we estimate the fractal dimension of stochastic processes with 1/f-like spectra by app...
In studying the scale invariance of an empirical time series a twofold problem arises: it is necessa...
In studying the scale invariance of an empirical time series a twofold problem arises: it is necessa...
A stochastic process Y (t) is defined as self-similar with self-similarity parameter H if for any po...
This paper presents a generalized approach to the fractal analysis of self-similar random processes ...
Abstract-The fractional Brownian motion (fBm) model has proven to be valuable in modeling many natur...
Statistical self-similarity of random processes in continuous-domains is defined through invariance ...
Introduction A stochastic process Y (t) is defined as self-similar with self-similarity parameter H...
A new method is proposed to estimate the self-similarity exponent. Instead of applying finite moment...
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