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Using non-stochastic terms to advantage in kernal-based estimation of integrated squared density derivatives

Jones, M. C. and Sheather, S. J. (1991). Using non-stochastic terms to advantage in kernal-based estimation of integrated squared density derivatives. Statistics and Probability Letters, 11(6) pp. 511–514.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1016/0167-7152(91)90116-9
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Abstract

Improved kernel-based estimates of integrated squared density derivatives are obtained by reinstating non-stochastic terms that have previously been omitted, and using the bandwidth to (approximately) cancel these positive quantities with the leading smoothing bias terms which are negative. Such estimators have exhibited great practical merit in the context of data-based selection of the bandwidth in kernel density estimation, a motivating application of this work discussed elsewhere.

Item Type: Journal Article
Copyright Holders: 1991 Elsevier Science B.V.
ISSN: 0167-7152
Keywords: bandwidth selection; bias reduction; functional estimation; kernel density estimation; rates of convergence; smoothing
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 28313
Depositing User: Sarah Frain
Date Deposited: 17 Mar 2011 13:42
Last Modified: 17 Mar 2011 13:42
URI: http://oro.open.ac.uk/id/eprint/28313
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