Robust statistical analysis of interlaboratory studies

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48 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)421-431
Number of pages11
Issue number2
StatePublished - Aug 1983


  • Biweight estimate
  • Huber estimate
  • Least squares
  • Monte Carlo method
  • Outlier
  • Random effects
  • Robust estimation
  • Variance component

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Mathematics(all)
  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)


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