Bump-hunting for the proficiency tester—searching for multimodality
- 3 September 2002
- journal article
- research article
- Published by Royal Society of Chemistry (RSC) in The Analyst
- Vol. 127 (10), 1359-1364
- https://doi.org/10.1039/b205600n
Abstract
Kernel density estimation is a method for producing a smooth density approximation to a dataset and avoiding some of the problems associated with histograms. If it is used with a degree of smoothing determined by a fitness for purpose criterion, it can be applied to proficiency test data in order to test for multimodality in the z-scores. The bootstrap is an essential additional technique to determine how rugged the initially estimated kernel density is: the random resampling of the data in the bootstrap simulates a complete blind repeat of the proficiency test. In addition, useful estimates of the standard error of a mode can be thus obtained. It is suggested that a mode and its standard error can be used as an assigned value and its standard uncertainty.Keywords
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