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Local smoothing with robustness against outlying predictors.

Einbeck, J. and Andre, C. D. S. and Singer, J. M. (2004) 'Local smoothing with robustness against outlying predictors.', Environmetrics., 15 (6). pp. 541-554.

Abstract

Outlying pollutant concentration data are frequently observed in time series studies conducted to investigate the effects of atmospheric pollution on mortality/morbidity. These outliers may severely affect the estimation procedures and even generate unexpected results like a protective effect of pollution. Although robust methods have been proposed to downweight the effect of outliers in the response variable distribution, little has been done to handle outlying explanatory variable values. We consider a robust local polynomial smoothing technique which may be useful for such purposes. It is based on downweighting points with a small design density and may also be used as a diagnostic tool to identify outliers. Using data from a study conducted in Sao Paulo, Brazil, we show how an unexpected form of the relative risk curve of mortality attributable to pollution by SO2 obtained via nonrobust methods may be completely reversed when the proposed technique is employed.

Item Type:Article
Additional Information:
Keywords:Atmospheric pollution, Nonparametric curve fitting, Outliers, Robust methods.
Full text:Full text not available from this repository.
Publisher Web site:http://dx.doi.org/10.1002/env.644
Record Created:25 Feb 2008
Last Modified:08 Apr 2009 16:30

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