Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 421-431 |
| Number of pages | 11 |
| Journal | Biometrika |
| Volume | 70 |
| Issue number | 2 |
| DOIs | |
| State | Published - Aug 1983 |
Keywords
- 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)