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Issue 10, 2002
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Bump-hunting for the proficiency tester—searching for multimodality

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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.

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The article was received on 10 Jun 2002, accepted on 08 Aug 2002 and first published on 03 Sep 2002

Article type: Paper
DOI: 10.1039/B205600N
Citation: Analyst, 2002,127, 1359-1364
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    Bump-hunting for the proficiency tester—searching for multimodality

    P. J. Lowthian and M. Thompson, Analyst, 2002, 127, 1359
    DOI: 10.1039/B205600N

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