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Local linear quantile regression

Yu, Keming and Jones, M. C. (1998). Local linear quantile regression. Journal of the American Statistical Association, 93(441) pp. 228–237.

URL: http://www.jstor.org/stable/2669619
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Abstract

In this article we study nonparametric regression quantile estimation by kernel weighted local linear fitting. Two such estimators are considered. One is based on localizing the characterization of a regression quantile as the minimizer of E{ρp (Y - a)|X = x}, where ρp is the appropriate "check" function. The other follows by inverting a local linear conditional distribution estimator and involves two smoothing parameters, rather than one. Our aim is to present fully operational versions of both approaches and to show that each works quite well; although either might be used in practice, we have a particular preference for the second. Our automatic smoothing parameter selection method is novel; the main regression quantile smoothing parameters are chosen by rule-of-thumb adaptations of state-of-the-art methods for smoothing parameter selection for regression mean estimation. The techniques are illustrated by application to two datasets and compared in simulations.

Item Type: Journal Article
Copyright Holders: 1998 American Statistical Association
ISSN: 1537-274X
Keywords: bandwidth selection; conditional quantile; kernel estimator; local linear regression; reference chart; rule of thumb
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 24037
Depositing User: Sarah Frain
Date Deposited: 05 May 2011 11:30
Last Modified: 05 May 2011 11:30
URI: http://oro.open.ac.uk/id/eprint/24037
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