Bootstrap error-adjusted single-sample technique

In statistics, the bootstrap error-adjusted single-sample technique (BEST) is a non-parametric method for estimating the distribution of a sample.^[1] It is based on a statistical method called bootstrapping. BEST provides advantages over other methods such as the Mahalanobis metric, because it does not assume equal covariance for all spectral groups^{[clarification needed]} or that each group is drawn for a normally distributed population.^[2]

Application

BEST is used in detection of sample tampering in pharmaceutical products. Valid (unaltered) samples are defined as those that fall inside the cluster of training-set points when the BEST is trained with unaltered product samples. False (tampered) samples are those that fall outside of the same cluster.^[1]

References

^ ^a ^b Lodder, R.A.; Selby, M.; Hieftje, G.A. (1987) Anal. Chem., 59, (15), 1921–1930 ^{[full citation needed]}
^ Attention: This template ({{cite jstor}}) is deprecated. To cite the publication identified by jstor:2685844, please use {{cite journal}} with |jstor=2685844 instead.

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[Lodder1987-1] Lodder, R.A.; Selby, M.; Hieftje, G.A. (1987) Anal. Chem., 59, (15), 1921–1930 ^{[full citation needed]}

[2] Attention: This template ({{cite jstor}}) is deprecated. To cite the publication identified by jstor:2685844, please use {{cite journal}} with |jstor=2685844 instead.

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