Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416367 | Computational Statistics & Data Analysis | 2014 | 12 Pages |
Abstract
Two classes of multiplicative bias correction (“MBC”) methods are applied to density estimation with support on [0,∞)[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)O(n−8/9) when best implemented, where nn 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.
Related Topics
Physical Sciences and Engineering
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Computational Theory and Mathematics
Authors
Masayuki Hirukawa, Mari Sakudo,