Kernel entropy estimation for linear processes II

Abstract: Let $X={X_n: n\in \mathbb{N}}$ be a linear process with bounded probability density function $f(x)$. Under certain conditions, we use the kernel estimator [ \frac{2}{n(n-1)h_n} \sum_{1\le i<j\le n}K\Big(\frac{X_i-X_j}{h_n}\Big) ] to estimate the quadratic functional of $\int_{\mathbb{R}}f^2(x)dx$ of the linear process $X={X_n: n\in \mathbb{N}}$ and improve the corresponding results in [4].