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Improved double kernel local linear quantile regression

Jones, M C and Yu, Keming (2007). Improved double kernel local linear quantile regression. Statistical Modelling, 7(4) pp. 377–389.

DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1177/1471082X0700700407
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Abstract

As sample quantiles can be obtained as maximum likelihood estimates of location parameters in suitable asymmetric Laplace distributions, so kernel estimates of quantiles can be obtained as maximum likelihood estimates of location parameters in a general class of distributions with simple exponential tails. In this paper, this observation is applied to kernel quantile regression. In doing so, a new double kernel local linear quantile regression estimator is obtained which proves to be consistently superior in performance to the earlier double kernel local linear quantile regression estimator proposed by the authors. It is also straightforward to compute and more readily affords a first derivative estimate. An alternative method of selection for one of the two bandwidths involved also arises naturally but proves not to be so consistently successful.

Item Type: Journal Article
Copyright Holders: 2007 SAGE Publications
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 22542
Depositing User: Sarah Frain
Date Deposited: 01 Sep 2010 16:13
Last Modified: 02 Dec 2010 21:01
URI: http://oro.open.ac.uk/id/eprint/22542
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