The Open UniversitySkip to content

A variation on local linear regression

Jones, M. C. (1997). A variation on local linear regression. Statistica Sinica, 7(4) pp. 1171–1180.

Google Scholar: Look up in Google Scholar


There has been much justifiable recent interest in local polynomial regression, and in particular in its local linear special case. Local linear regression has advantages in terms of desirable theoretical properties both in the interior and near the boundaries of the region of interest. For implementation, binning is useful. In this paper, we describe a variation on local linear regression which can be considered an alternative binning thereof. We show that existing and novel methods are almost indistinguishable. The point of the paper is not to extol the virtues of the new version over the old, but rather (i) to show that the good properties of local linear regression can be achieved in more than one way, and (ii) to elucidate close links between local linear regression and other kernel smoothing methods. The latter include, most closely, a boundary corrected ‘naive’ kernel estimator and a recent proposal of Wu and Chu (1992), as well as binned Nadaraya–Watson estimators and methods for binomial regression.

Item Type: Journal Item
Copyright Holders: 1997 Statistic Sinica
ISSN: 1017-0405
Keywords: binning, boundary correction, kernel smoothing, nonparametric regression
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 24105
Depositing User: Sarah Frain
Date Deposited: 11 May 2011 11:13
Last Modified: 07 Dec 2018 09:42
Share this page:

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU