Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15195
Title: Finding environmental factors for respiratory diseases by nonparametric variable selection
Authors: Wong, H 
Ip, WC
Zhang, R
Keywords: Additive models
Air pollution
Bootstrap test
Local linear method
Respiratory diseases
Variable selection
Issue Date: 2009
Publisher: Elsevier Science Bv
Source: Science of the total environment, 2009, v. 407, no. 14, p. 4303-4311 How to cite?
Journal: Science of the Total Environment 
Abstract: It is well known that the exposure to ambient air pollution might cause serious respiratory illnesses and that the weather conditions may also contribute to the seriousness. However, quantifying the effects of pollution and the weather condition is a difficult task due to the nonlinear nature of these impacts. The problem is further complicated by the possibly cumulative effects of these impacts. In this paper, the nonparametric additive (NPA) models, which have the advantage of ease in interpretation and forecasting, are employed for modeling the effects of pollution and weather. All models are derived by the local linear method. The variables in the final selected NPA model are chosen by cross-validation method together with bootstrap test for the data of Hong Kong. For comparison the final selected linear regression (LR) model by the backward elimination method is also considered. It is found, interestingly, that the variables selected by nonparametric method and the usual backward elimination method for linear models are different. Furthermore, by comparing forecasted values obtained from the NPA and LR models and true values the final selected NPA model is shown to outperform the LR model.
URI: http://hdl.handle.net/10397/15195
DOI: 10.1016/j.scitotenv.2009.03.027
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