Complete convergence for moving average processes associated to heavy-tailed distributions and applications

Complete convergence of moving-average processes under negative dependence sub-Gaussian assumptions

The complete convergence is investigated for moving-average processes of doubly infinite sequence of negative dependence sub-gaussian random variables with zero means, finite variances and absolutely summable coefficients. As a corollary, the rate of complete convergence is obtained under some suitable conditions on the coefficients.

Complete convergence of moving average processes under dependence assumptions 1

Let {Yi;-oc < i < c~} be a doubly infinite sequence of identically distributed and (b-mixing random variables, (ai; ~ < i < oc} an absolutely summable sequence of real numbers. In this paper, we prove the complete convergence of {Ek=xn ~io~=_¢xz ai+kYi/nt/,; n>~ 1} under some suitable conditions. AMS classification: 60G50; 60F15

complete convergence of moving-average processes under negative dependence sub-gaussian assumptions

the complete convergence is investigated for moving-average processes of doubly infinite sequence of negative dependence sub-gaussian random variables with zero means, finite variances and absolutely summable coefficients. as a corollary, the rate of complete convergence is obtained under some suitable conditions on the coefficients.

complete convergence of moving-average processes under negative dependence sub-gaussian assumptions

the complete convergence is investigated for moving-average processes of doubly infinite sequence of negative dependence sub-gaussian random variables with zero means, finite variances and absolutely summable coefficients. as a corollary, the rate of complete convergence is obtained under some suitable conditions on the coefficients.

Complete Convergence for Moving Average Process of Martingale Differences