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Robust Statistical Analysis of Interlaboratory Studies
David M. Rocke
Vol. 70, No. 2 (Aug., 1983), pp. 421-431
Stable URL: http://www.jstor.org/stable/2335556
Page Count: 11
You can always find the topics here!Topics: Statistical variance, Least squares, Estimation methods, Statistical estimation, Standard deviation, Outliers, Laboratory techniques, Analytical estimating, Statism, Statistical analysis
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A common procedure in testing analytical methods is to send a portion of each of a number of samples to each of several laboratories. The results of such a study are submitted to statistical analysis to determine the two important variance components in the problem: replication error and laboratory bias. Outliers are relatively common in these data both among laboratory effects and among the residuals. This paper presents a method of analysis for interlaboratory studies that is robust to the existence of outliers and long-tailed distributions of random effects. Theoretical considerations as well as a Monte Carlo study are adduced as support for this new technique.
Biometrika © 1983 Biometrika Trust