AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are established for the spectral density of certain stationary stable processes. This spectral density plays a role, in linear inference problems, analogous to that played by the usual power spectral density of second order stationary processes
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
Weakly and strongly consistent nonparametric estimates, along with rates of convergence, are establi...
International audienceIn this paper, a symmetric alpha stable process where its spectral representat...
International audienceIn this paper, a symmetric alpha stable process where its spectral representat...
International audienceIn this paper, a symmetric alpha stable process where its spectral representat...
We consider a stationary symmetric stable bidimensional process with discrete time, having the spect...
We considered a complex strongly harmonizable stationary symmetric stable process in continuous time...
AbstractThis paper deals with issues pertaining to estimating the spectral density of a stationary h...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
A stationary Gaussian process is said to be long-range dependent (resp., anti-persistent) if its spe...
Applications of p-adic numbers ar beming increasingly important espcially in the field of applied ph...
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
Weakly and strongly consistent nonparametric estimates, along with rates of convergence, are establi...
International audienceIn this paper, a symmetric alpha stable process where its spectral representat...
International audienceIn this paper, a symmetric alpha stable process where its spectral representat...
International audienceIn this paper, a symmetric alpha stable process where its spectral representat...
We consider a stationary symmetric stable bidimensional process with discrete time, having the spect...
We considered a complex strongly harmonizable stationary symmetric stable process in continuous time...
AbstractThis paper deals with issues pertaining to estimating the spectral density of a stationary h...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
A stationary Gaussian process is said to be long-range dependent (resp., anti-persistent) if its spe...
Applications of p-adic numbers ar beming increasingly important espcially in the field of applied ph...
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
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