Question

Problem 5.2 Mean and variance of a kernel density estimate

In this problem, we will study the mean and variance of the kernel density estimate, i.e., the distribution $\hat{p}(x)$. Let $X = \{x_1, \cdots, x_n\}$ be the set of samples, and $\tilde{k}(x)$ be the kernel with bandwidth included. The estimated probability distribution is $\hat{p}(x) = \frac{1}{n} \sum*{i=1}^n \tilde{k}(x - x_i). \quad (5.5)$ Suppose that the kernel function $\tilde{k}(x)$ has zero mean and covariance $H$, i.e., $\mathbb{E}*{\tilde{k}}[x] = \int \tilde{k}(x) x dx = 0, \quad (5.6)$ $\operatorname{cov}_{\tilde{k}}(x) = \int \tilde{k}(x) (x - \mathbb{E}_{\tilde{k}}[x])(x - \mathbb{E}\_{\tilde{k}}[x])^T dx = H. \quad (5.7)$

(c) What does this tell you about the properties of the kernel density estimate $\hat{p}(x)$? How does this relate to the bias of the kernel density estimator?