title: Modeling uncertainty in the measurement of low-level analytes in environmental analysis.
abstract: 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.
authors.affiliation: Department of Applied Science and Division of Biostatistics, University of California, Davis, CA 95616, USA. dmrocke@ucdavis.edu
authors.forename: David M
authors.initials: DM
authors.lastname: Rocke
authors.forename: Blythe
authors.initials: B
authors.lastname: Durbin
authors.forename: Machelle
authors.initials: M
authors.lastname: Wilson
authors.forename: Henry D
authors.initials: HD
authors.lastname: Kahn
mesh_terms: 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
chemical_list: D004785:Environmental Pollutants