Local Smoothing with Robustness against outlying Predictors

Einbeck, Jochen; Andre, Carmen D.S.; Singer, Julio M.

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.

Citation

Einbeck, J., Andre, C. D., & Singer, J. M. (2004). Local Smoothing with Robustness against outlying Predictors. Environmetrics, 15(6), 541-554. https://doi.org/10.1002/env.644

• Journal Article Type: Article
• Publication Date: 2004-09
• Deposit Date: Feb 25, 2008
• Journal: Environmetrics
• Print ISSN: 1180-4009
• Electronic ISSN: 1099-095X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 6
• Pages: 541-554
• DOI: https://doi.org/10.1002/env.644
• Keywords: Atmospheric pollution, Nonparametric curve fitting, Outliers, Robust methods.
• Public URL: https://durham-repository.worktribe.com/output/1547335
• Publisher URL: http://www3.interscience.wiley.com/cgi-bin/abstract/109084070/ABSTRACT