Add to ORKGAbstract In a recent paper Eichler (2008) considered a class of non-and semiparametric hypotheses in multivariate stationary processes, which are characterized by a functional of the spectral density matrix. The corresponding statistics are obtained using kernel estimates for the spectral distribution and are asymptotically normal distributed under the null hypothesis and local alternatives. In this paper we derive the asymptotic properties of these test statistics under fixed alternatives. In particular we show also weak convergence but with a different rate compared to the null hypothesis
This paper establishes the weak convergence of a class of marked empirical processes of possibly non...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
We derive uniform convergence results of lag-window spectral density estimates for a general class o...
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
AbstractWe propose a general nonparametric approach for testing hypotheses about the spectral densit...
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
AbstractWe propose a general nonparametric approach for testing hypotheses about the spectral densit...
The study of locally stationary processes contains theory and methods about a class of processes tha...
This paper establishes the weak convergence of a class of marked empirical processes of possibly non...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
We derive uniform convergence results of lag-window spectral density estimates for a general class o...
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
We propose a general nonparametric approach for testing hypotheses about the spectral density matrix...
AbstractWe propose a general nonparametric approach for testing hypotheses about the spectral densit...
AbstractIn a recent paper, Eichler (2008) [11] considered a class of non- and semiparametric hypothe...
AbstractWe propose a general nonparametric approach for testing hypotheses about the spectral densit...
The study of locally stationary processes contains theory and methods about a class of processes tha...
This paper establishes the weak convergence of a class of marked empirical processes of possibly non...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
We derive uniform convergence results of lag-window spectral density estimates for a general class o...