Jones, M. C. and Signorini, D. F.
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We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as the smoothing parameter h → 0, to O(h4). Examples include higher-order kernels, variable kernel methods, and transformation and multiplicative bias-correction approaches. We stress the similarities between what appear to be disparate approaches. In particular, we show how the mean squared errors of all methods have the same form. Our main practical contribution is a comparative simulation study that isolates the most promising approaches. It remains debatable, however, as to whether even the best methods give worthwhile improvements, at least for small-to-moderate sample exploratory purposes.
|Item Type:||Journal Article|
|Copyright Holders:||1997 American Statistical Association|
|Keywords:||bias reduction; higher-order kernel; multiplicative bias correction; smoothing; transformation; variable band- width; variable location|
|Academic Unit/Department:||Mathematics, Computing and Technology > Mathematics and Statistics|
|Depositing User:||Sarah Frain|
|Date Deposited:||11 May 2011 12:26|
|Last Modified:||11 May 2011 12:26|
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