Multiplicative bias correction for asymmetric kernel density estimators revisited

摘要

Multiplicative bias correction technique is revisited for asymmetric kernel density estimators (KDEs) when the data is nonnegative or bounded. It is crucial to classify the recently developed asymmetric KDEs into two types. The multiplicative bias correction applied to the non two-regime type is shown to effectively reduce the order of the bias, at the expense of a constant-factor inflation of the variance. However, it is revealed that, in common with other bias corrections, the multiplicative bias correction applied to the two-regime type fails in reducing the bias near the boundary, unless the density to be estimated satisfies the shoulder condition. (C) 2019 Elsevier B.V. All rights reserved.