Directory UMM :Data Elmu:jurnal:S:Stochastic Processes And Their Applications:Vol87.Issue2.2000:
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Keywords: Locally stationary processes; Nonlinear thresholding; Nonparametric curve estimation; Preperiodogram; Time series; Wavelet
The tail sum of method B is asymptotically Gaussian and since we know that the tail sum has a class G distribution this implies that the normalised mixing distribution converges
The performance of the algorithm is strongly linked to the presence or absence of phase transition for the Gibbs distribution; the convergence time being approximately linear
Assuming that the corresponding imbedded planar point processes converge to a Poisson process we introduce some additional conditions which allow to approximate the optimal
Thus the non-uniqueness of solutions to stochastic dierential equations can be viewed as a special case of the non-uniqueness of ordinary dierential equations with measure
In this paper a class of small deviation theorems (i.e., the strong limit theorems rep- resented by inequalities) for the averages of the bivariate functions of the sequences
We propose a class of Dirichlet forms corresponding to spatially inhomogeneous processes which covers a family of Dirichlet forms described by modied derivatives similarly to the
To begin, we state a technical Lemma, involving controls on the law of some killed processes: this result is crucial to handle the case of measurable functions f for the analysis of
