The aim of this paper is to estimate the Hurst parameter of Fractional Gaussian Noise (FGN) using Bayesian inference. We propose an estimation technique that takes into account the full correlation structure of this process. Instead of using the integrated time series and then applying an estimator for its Hurst exponent, we propose to use the noise signal directly. As an application we analyze the time series of the Nile River, where we find a posterior distribution which is compatible with previous findings. In addition, our technique provides natural error bars for the Hurst exponent
The estimation of long-memory processes has been studied from different perspectives: non-parametric...
Fractal investigation of time series is very complex for several reasons. Due to the existence of fu...
Abstract- The intensity of long-range dependence (LRD) of the communications network traffic can be ...
We present a new method to estimate the Hurst parameter. The method exploits the form of the autocor...
D.Phil. (Mathematical Statistics)Fractional Brownian motion and its increment process, fractional Ga...
We consider a model based on the fractional Brownian motion under the influence of noise. We impleme...
A maximum likelihood estimation method implemented in S-PLUS (S-MLE) to estimate the Hurst coefficie...
In this paper, we build an estimator of the Hurst exponent of a fractional Lévy motion based on its ...
Fractional Gaussian noise (fGn) is a stationary stochastic process used to model anti-persistent or...
peer reviewedWe present a Bayesian Monte Carlo Markov Chain method to simultaneously estimate the sp...
International audienceThe use of diffusion models driven by fractional noise has become popular for ...
In the paper consistent estimates of the Hurst parameter of fractional Brownian motion are obtained ...
Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speed...
The objective of this thesis is to develop and refine statistical methods which can be used for solv...
International audienceIn the modern world of "Big Data," dynamic signals are often multivariate and ...
The estimation of long-memory processes has been studied from different perspectives: non-parametric...
Fractal investigation of time series is very complex for several reasons. Due to the existence of fu...
Abstract- The intensity of long-range dependence (LRD) of the communications network traffic can be ...
We present a new method to estimate the Hurst parameter. The method exploits the form of the autocor...
D.Phil. (Mathematical Statistics)Fractional Brownian motion and its increment process, fractional Ga...
We consider a model based on the fractional Brownian motion under the influence of noise. We impleme...
A maximum likelihood estimation method implemented in S-PLUS (S-MLE) to estimate the Hurst coefficie...
In this paper, we build an estimator of the Hurst exponent of a fractional Lévy motion based on its ...
Fractional Gaussian noise (fGn) is a stationary stochastic process used to model anti-persistent or...
peer reviewedWe present a Bayesian Monte Carlo Markov Chain method to simultaneously estimate the sp...
International audienceThe use of diffusion models driven by fractional noise has become popular for ...
In the paper consistent estimates of the Hurst parameter of fractional Brownian motion are obtained ...
Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speed...
The objective of this thesis is to develop and refine statistical methods which can be used for solv...
International audienceIn the modern world of "Big Data," dynamic signals are often multivariate and ...
The estimation of long-memory processes has been studied from different perspectives: non-parametric...
Fractal investigation of time series is very complex for several reasons. Due to the existence of fu...
Abstract- The intensity of long-range dependence (LRD) of the communications network traffic can be ...