Kernel density estimation for stationary random fields

Details Kernel density estimation for stationary random fields Kernel density estimation for stationary random fields

2011-09-13

arxiv.org/abs/1109.2694v2

Mathematics

Statistics Theory

22 pages

Scientific paper

In this paper, under natural and easily verifiable conditions, we prove the $\mathbb{L}^1$-convergence and the asymptotic normality of the Parzen-Rosenblatt density estimator for stationary random fields of the form $X_k = g(\varepsilon_{k-s}, s \in \Z^d)$, $k\in\Z^d$, where $(\varepsilon_i)_{i\in\Z^d}$ are i.i.d real random variables and $g$ is a measurable function defined on $\R^{\Z^d}$. Such kind of processes provides a general framework for stationary ergodic random fields. A Berry-Esseen's type central limit theorem is also given for the considered estimator.

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