Add to ORKGThe purpose of this paper is to study the self-similar properties of discrete-time long memory processes. We apply our results to specific processes such as GARMA processes and GIGARCH processes, heteroscedastic models and the processes with switches and jumps.Covariance stationary, Long memory processes, short memory processes, self-similar, asymptotically second-order self-similar, autocorrelation function.
We study problems of semiparametric statistical inference connected with long-memory covariance stat...
AbstractThis paper points out that there exists a unique optimal approximation of autocorrelation fu...
We study the concept of self-similarity with respect to stochastic time change. The negative binomia...
The purpose of this paper is to study the self-similar properties of discrete-time long memory proce...
URL des Documents de travail :http://ces.univ-paris1.fr/cesdp/CESFramDP2007.htmClassification JEL : ...
To cite this version: Dominique Guegan, Zhiping Lu. A note on self-similarity for discrete time seri...
Financial and seismic data, like many other high frequency data are known to exhibit memory effects....
Financial and seismic data, like many other high frequency data are known to exhibit memory effects....
This work is concerned with the analysis of self-similar stochastic pro-cesses, where statistical se...
AbstractA self-similar process Z(t) has stationary increments and is invariant in law under the tran...
Abstract: This note surveys some recent results on self-similar Markov processes. Since the research...
Statistical self-similarity of random processes in continuous-domains is defined through invariance ...
We propose a method to analyze a turbulent sequence focusing on the self-similar properties of the d...
A classical result, due to Lamperti, establishes a one-to-one correspondence between a class of stri...
We define a new type of self-similarity for one-parameter families of stochastic processes, which ap...
We study problems of semiparametric statistical inference connected with long-memory covariance stat...
AbstractThis paper points out that there exists a unique optimal approximation of autocorrelation fu...
We study the concept of self-similarity with respect to stochastic time change. The negative binomia...
The purpose of this paper is to study the self-similar properties of discrete-time long memory proce...
URL des Documents de travail :http://ces.univ-paris1.fr/cesdp/CESFramDP2007.htmClassification JEL : ...
To cite this version: Dominique Guegan, Zhiping Lu. A note on self-similarity for discrete time seri...
Financial and seismic data, like many other high frequency data are known to exhibit memory effects....
Financial and seismic data, like many other high frequency data are known to exhibit memory effects....
This work is concerned with the analysis of self-similar stochastic pro-cesses, where statistical se...
AbstractA self-similar process Z(t) has stationary increments and is invariant in law under the tran...
Abstract: This note surveys some recent results on self-similar Markov processes. Since the research...
Statistical self-similarity of random processes in continuous-domains is defined through invariance ...
We propose a method to analyze a turbulent sequence focusing on the self-similar properties of the d...
A classical result, due to Lamperti, establishes a one-to-one correspondence between a class of stri...
We define a new type of self-similarity for one-parameter families of stochastic processes, which ap...
We study problems of semiparametric statistical inference connected with long-memory covariance stat...
AbstractThis paper points out that there exists a unique optimal approximation of autocorrelation fu...
We study the concept of self-similarity with respect to stochastic time change. The negative binomia...