Affordable Access

On the asymptotic normality of kernel density estimators for linear random fields

Authors
  • Wang, Yizao
  • Woodroofe, Michael
Type
Preprint
Publication Date
Dec 31, 2011
Submission Date
Dec 31, 2011
Identifiers
arXiv ID: 1201.0238
Source
arXiv
License
Yellow
External links

Abstract

We establish sufficient conditions for the asymptotic normality of kernel density estimators, applied to causal linear random fields. Our conditions on the coefficients of linear random fields are weaker than known results, although our assumption on the bandwidth is not minimal. The proof is based on the $m$-approximation method. As a key step, we prove a central limit theorem for triangular arrays of stationary $m$-dependent random fields with unbounded $m$. We also apply a moment inequality recently established for stationary random fields.

Report this publication

Statistics

Seen <100 times