Directory UMM :Data Elmu:jurnal:S:Stochastic Processes And Their Applications:Vol90.Issue1.2000:
Teks penuh
Dokumen terkait
We study the asymptotic behaviour of non-linear functionals of regularizations by convolution of this process and apply these results to the estimation of the variance of
other examples, such as the convergence of slice samplers (Roberts and Rosenthal, 1999), this extra condition does not alter the bound on the actual convergence time (see Theorem 12
The topic of estimating rates of convergence in the functional central limit theorem (FCLT) for martingales has been studied for a long time, but optimal results were found only
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
Following the strategy adopted in Section 4 for the Kawasaki dynamics, we divide the proof of Theorem 6.1 into three steps: Tightness, identication of the limit, and under
Hong (1995) has shown that if X 1 ; X 2 ; : : : are jointly symmetric, pairwise independent and identically distributed with a nite second moment, then the central limit theorem
We rst introduce the class of Markov processes with jumps for which one can apply the result of Picard (1996) for the existence of smooth densities and of Picard (1997b) for
Combining a classical inequality of Zygmund [19] with the best constant found by Pichorides [16, Theorem 3.4], we have:..
