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Local Smoothing with Robustness against outlying Predictors

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

Authors

Carmen D.S. Andre

Julio M. Singer



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.
Publisher URL http://www3.interscience.wiley.com/cgi-bin/abstract/109084070/ABSTRACT