Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting

• Published in: Journal of agricultural, biological, and environmental statistics Vol. 20; no. 1; pp. 100 - 120
• Main Authors: Carrico, Caroline, Gennings, Chris, Wheeler, David C., Factor-Litvak, Pam
• Format: Journal Article
• Language: English
• Published: Boston Springer Science+Business Media, LLC 01.03.2015; Springer US; Springer
• Subjects: Actors; Agriculture; Biostatistics; Chemical properties; Health Sciences; inflation; Inflation (Economics); Mathematics and Statistics; Medicine; Monitoring/Environmental Analysis; regression analysis; Risk assessment; shrinkage; Statistics; Statistics for Life Sciences; variance; WQS; Correlation; Nonlinear model; Subset selection; Variable selection

In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinea...