Department of Statistics , University of Missouri-Columbia , Columbia, MO, 65211
Abstract:
An approach for removing boundary bias in nonparametric density esti-mation is considered. The technique is based on suitable finite-dimensional projections in Hilbert space. Applications to boundary bias removal with kernel and trigonometric series estimators are presented.