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Activity Number: 337 - Environmental Epidemiology and Analysis of Large Database
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #322837
Title: Nonparametric Screening and Selection in Presence of Dependence Among Predictors
Author(s): Shanta Ghosh* and Sanjib Basu
Companies: University of Illinois at Chicago and Biostatistics, University of Illinois Chicago
Keywords: Variable selection; Environmental exposures; Collinearity; High dimensional; Distance Correlation
Abstract:

Variable selection under multicollinearity is well studied but remains challenging. Our motivating application arises in environmental epidemiology where individuals are exposed simultaneously to a multitude of pollutants in the environmental mixture that potentially interact and present a health risk. The pollutant measures are often highly correlated at levels that are generally not seen in other areas of science. We develop a model-free screening and selection method using distance correlation, which is a non-parametric measure of dependence in arbitrary dimensions. We compare performance with existing methods under linear and nonlinear data-generating models. We apply the proposed method to environmental mixtures data in NHANES involving many strongly correlated persistent organic pollutants.


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