m(n)-相依样本的经验分布函数及其在Hazard估计中的应用
Empirical Distribution Function for m(n)-dependent Sample and Its Application to Hazard’s Estimate
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摘要: 设Xn为随机变量X的观察序列,Xn不必相互独立。本文在Xn为m(n)相依条件下得到了Glivenko-Cantelli定理的一种推广,并获得了密度函数核估计在紧集上的一致收敛速度,这两个结果合并导出Hazard核估计在紧集上的一致收敛速度。Abstract: Let X be a random variable and Xn a sequence of realizations of X which are not necessarily assumed to be independent. We derive a generalization of of Glivenko-Cantelli theorem and the uniform rate of convergence on a compact set of density kernel estimate under the condition that Xn is a m(n)-dependent sequence. These results together lead to uniform rate of convergence of hazard kernel estimate.