February 2013We consider the model for the discrete nonboundary wavelet coefficients of ARFIMA processes. Although many authors have explained the utility of the wavelet transform for the long dependent processes in semiparametrical literature, there have been a few studies in parametric setting. In this paper, we restrict the Daubechies wavelets filters to make the form of the (general) spectral density function of these coefficients clear.グローバルCOEプログラム = Global COE Program17 p
The theme of our work focuses on statistical process long memory, for which we propose and validate ...
Wavelet methods have proven useful in the analysis and synthesis of one-dimensional processes (e.g.,...
Autocorrelation and non-normality of process characteristic variables are two main difficulties that...
In this paper we apply compactly supported wavelets to the ARFIMA(p,d,q) long-memory process to deve...
In this research, we explore the applications of wavelet theory in nonparametric regression and dens...
By design a wavelet's strength rests in its ability to localize a process simultaneously in time-sca...
. We consider nonparametric estimation of the parameter functions a i (\Delta) , i = 1; : : : ; p ,...
In this paper we give the main uses of wavelets in statistics, with emphasis in time series analysis...
ACL-3International audienceIn this article, we propose two new semiparametric estimators in the wave...
International audienceThis paper is a contribution to the analysis of the statistical correlation of...
Spectral density built as Fourier transform of covariance sequence of stationary random process is ...
This paper addresses the limit distribution of wavelet packet coefficients obtained by decomposing b...
We consider nonparametric estimation of the parameter functions a(i)(.), i = 1, ..., p, of a time-va...
International audienceThis paper is a contribution to the analysis of the statistic al correla- tion...
We propose new wavelet-based procedure to estimate the memory parameter of an unobserved process fro...
The theme of our work focuses on statistical process long memory, for which we propose and validate ...
Wavelet methods have proven useful in the analysis and synthesis of one-dimensional processes (e.g.,...
Autocorrelation and non-normality of process characteristic variables are two main difficulties that...
In this paper we apply compactly supported wavelets to the ARFIMA(p,d,q) long-memory process to deve...
In this research, we explore the applications of wavelet theory in nonparametric regression and dens...
By design a wavelet's strength rests in its ability to localize a process simultaneously in time-sca...
. We consider nonparametric estimation of the parameter functions a i (\Delta) , i = 1; : : : ; p ,...
In this paper we give the main uses of wavelets in statistics, with emphasis in time series analysis...
ACL-3International audienceIn this article, we propose two new semiparametric estimators in the wave...
International audienceThis paper is a contribution to the analysis of the statistical correlation of...
Spectral density built as Fourier transform of covariance sequence of stationary random process is ...
This paper addresses the limit distribution of wavelet packet coefficients obtained by decomposing b...
We consider nonparametric estimation of the parameter functions a(i)(.), i = 1, ..., p, of a time-va...
International audienceThis paper is a contribution to the analysis of the statistic al correla- tion...
We propose new wavelet-based procedure to estimate the memory parameter of an unobserved process fro...
The theme of our work focuses on statistical process long memory, for which we propose and validate ...
Wavelet methods have proven useful in the analysis and synthesis of one-dimensional processes (e.g.,...
Autocorrelation and non-normality of process characteristic variables are two main difficulties that...