Modeling uncertainty in the measurement of low-level analytes in environmental analysis.
The use of analytical chemistry measurements in environmental monitoring is dependent on an assessment of measurement error. Models for variation in measurements are needed to quantify uncertainty in measurements, set limits of detection, and preprocess data for more sophisticated analysis in prediction, classification, and clustering. This article explains how a two-component error model can be used to accomplish all of these objectives. In addition, we present applications to quantitating biomarkers of exposure to toxic substances using gene expression microarrays.
Rocke
David M
DM
Department of Applied Science and Division of Biostatistics, University of California, Davis, CA 95616, USA. dmrocke@ucdavis.edu
Durbin
Blythe
B
Wilson
Machelle
M
Kahn
Henry D
HD
D002623:Chemistry Techniques, Analytical / Q000592:standards* / Q000706:statistics & numerical data*; D004785:Environmental Pollutants / Q000633:toxicity*; D008962:Models, Theoretical; D015203:Reproducibility of Results; D018570:Risk Assessment
D004785:Environmental Pollutants