Year: 2012
Type: preprint
Abstract: Linear wavelet density estimators are wavelet projections of the empirical measure based on independent, identically distributed observations. We study here the law of the iterated logarithm (LIL) and a Berry-Esseen type theorem. These results are proved under different assumptions on the density $f$ that are different from those needed for similar results in the case of convolution kernels (KDE): whereas the smoothness requirements are much less stringent than for the KDE, Riemann integrability assumptions are needed in order to compute the asymptotic variance, which gives the scaling constant in LIL. To study the Berry-Esseen type theorem, a rate of convergence result in the martingale CLT is used.
Source: arXiv (Cornell University)
Author: Lu, Lu
Institution: University of Connecticut
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2951227909