Using supervised principal components analysis to assess multiple pollutant effects
BACKGROUND: Many investigations of the adverse health effects of multiple air pollutants analyze the time series involved by simultaneously entering the multiple pollutants into a Poisson log-linear model. This method can yield unstable parameter estimates when the pollutants involved suffer high intercorrelation ; therefore, traditional approaches to dealing with multicollinearity, such as principal component analysis (PCA) , have been promoted in this context. OBJECTIVES: A characteristic...[Show more]
|Collections||ANU Research Publications|
|Source:||Environmental Health Perspectives|
|Roberts_UsingSupervised2006.pdf||526.17 kB||Adobe PDF|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.