In this paper, the authors prove an almost sure limit theorem for the maxima of non-stationary Caussian random fields under some mild conditions related to the covariance functions of the Gaussian fields. As the by-products, the authors also obtain several weak convergence results which extended the existing results.
We establish some strong limit theorems for a sequence of pair-wise extended lower/upper negatively dependent random variables and give some new examples of dependent random variables.
设{X_i}_(i=1)~∞是标准化强相依非平稳高斯序列,记S_n=sum from i=1 to n X_i,σ_n=(var(Sn))～(1/2)M_(t_n)~k为X_1,X_2,…,X_(t_n)的第k个最大值,N_(t_n)为X_1,X_2,…,X_(t_n)对水平μ_n(x)的超过数形成的点过程,t_n是一列单调增加的正整数列,在一定条件下得到N_(t_n)与S_n/σ_n,M_(t_n)~k与S_n/σ/n的联合渐近分布.
Let （X, Xk ： k ≥ 1） be a sequence of extended negatively dependent random variables with a common distribution F satisfying EX 〉 0.Let τ be a nonnegative integer-valued random variable, independent of {X, Xk ： k ≥ 1}. In this paper, the authors obtain the necessary and sufficient conditions for the random sums Sτ=∑n=1^τ Xn to have a consistently varying tail when the random number τ has a heavier tail than the summands, i.e.,P（X〉x）/P（τ〉x）→0 as x →∞.
