A criterion for hypothesis testing for stationary processes

The product form of the stationary distribution for a two-component Markov process is obvious if the two compo- nents are independent Markov processes.. The product form is far

In [8] we showed that even when we restrict to binary processes the class of all finite order Markov chains cannot be distinguished from its complement by any weakly consistent

Therefore, the null distribution of the resulting test statistic coincides with that of a comparison of margins, as in [4] or [3], and we can use their previous results to obtain

• Without any prior knowledge of the signal distribution, SeqRDT is shown to guarantee pre-specified PFA and PMD, whereas, in contrast, the likelihood ratio based tests need

Our results apply to a number of examples: sums of stationary correlated random variables whose scaling limit is fractional Brownian motion; random walks in random sceneries;

Sufficient conditions for the continuity of stationary gaussian processes and applications to random series of functions.. Annales de l’institut Fourier, tome 24, n o 2

In the sequel, we will build again the spectral measure for invertible transformation and reversible Markov chain and then apply to Ergodic theorem and Central limit theorem..

We recover the optimal adaptive rates of convergence over Besov spaces (that is the same as in the independent framework) for τ -mixing processes, which is new as far as we know..