Nonnegative bias reduction methods for density estimation using asymmetric kernels

Two classes of multiplicative bias correction (‘‘MBC’’) methods are applied to density estimation with support on [0, ∞). It is demonstrated that under sufficient smoothness of the true density, each MBC technique reduces the order of magnitude in bias, whereas the order of magnitude in variance remains unchanged. Accordingly, the mean integrated squared error of each MBC estimator achieves a faster convergence rate of O  n−8/9  when best implemented, where n is the sample size. Furthermore, MBC estimators always generate nonnegative estimates by construction. Plug-in smoothing parameter choice rules for the estimators are proposed, and their finite sample performance is examined via Monte Carlo simulations. © 2014 Elsevier B.V. All rights reserved.