NA随机变量的递归密度核估计的渐近正态性

Asymptotic Normality of the Recursive Kernel Estimate of a Probability Density Function under Negatively Associated

设{Xn,n≥1}为同分布的NA样本序列,其未知概率密度函数为f(x),基于样本X1,…,Xn,用递归密度核估计对f(x)进行估计.本文研究了在一定条件下,fn(x)的渐近正态性.

Let X1,… ,Xn be negatively associated (NA) random samples from an unknown density function f(x). The recursive kernel density function estimator can be obtained by putting In the paper, we will discuss the asymptotic normality for density estimator fn(x) under suitable condition.