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

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 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

On the Precise Asymptotics in Complete Moment Convergence of Moving Average Processes under NA Random Variables

Let {ε i | − ∞ < i < ∞} be a sequence of identically distributed negatively associated random variables and {a i | − ∞ < i < ∞} a sequence of real numbers with

Moving Average Processes with Infinite Variance

The sample autocorrelation function (acf) of a stationary process has played a central statistical role in traditional time series analysis, where the assumption is made that the marginal distribution has a second moment. Now, the classical methods based on acf are not applicable in heavy tailed modeling. Using the codifference function as dependence measure for such processes be shown it be as...

Complete Convergence for Moving Average Process of Martingale Differences