Jones, M. C.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1111/1467-9574.00124|
|Google Scholar:||Look up in Google Scholar|
In nonparametric estimation of functionals of a distribution, it may or may not be desirable, or indeed necessary, to introduce a degree of smoothing into this estimation. In this article, I describe a method for assessing, with just a little thought about the functional of interest, (i) whether smoothing is likely to prove worthwhile, and (ii) if so, roughly how much smoothing is appropriate (in order-of-magnitude terms). This rule-of-thumb is not guaranteed to be accurate nor does it give a complete answer to the smoothing problem. However, I have found it very useful over a number of years; many examples of its use, and limitations, are given.
|Item Type:||Journal Article|
|Copyright Holders:||2000 VVS|
|Keywords:||bandwidth;density derivatives;empirical distribution function;kernel density estimation;kernel functional estimation;mean squared error;smoothed bootstrapping|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||05 Apr 2011 13:59|
|Last Modified:||02 Aug 2016 13:48|
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