We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance stationary process with spectral density. We investigate optimal rates of convergence for the estimators of ω0 and d, and the consequence that the lack of knowledge of ω0 has on the estimation of the memory parameter d. We present estimators which achieve the optimal rates. A small Monte-Carlo study is included to illustrate the finite sample performance of our estimators
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
We consider parameter estimation for time-dependent locally stationary long-memory processes. The as...
International audienceSome convergence results on the kernel density estimator are proven for a clas...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter, ?0 and a respectively, ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
We consider parameter estimation for time-dependent locally stationary long-memory processes. The as...
International audienceSome convergence results on the kernel density estimator are proven for a clas...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter, ?0 and a respectively, ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
Some convergence results on the kernel density estimator are proven for a class of linear processes ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
We consider the estimation of the location of the pole and memory parameter, λ0 and α respectively, ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
This paper provides limit theorems for spectral density matrix estimators and functionals of it for ...
We consider parameter estimation for time-dependent locally stationary long-memory processes. The as...
International audienceSome convergence results on the kernel density estimator are proven for a clas...