Bootstrap error-adjusted single-sample technique
 | This article relies largely or entirely on a single source. (February 2011) |
In statistics, the bootstrap error-adjusted single-sample technique (BEST or the BEAST) 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]A quantitative approach involves BEST along with nonparametric cluster analysis algorithm and multidimensional standard deviations (MDSs) between clusters and spectral data points can be calculated, where BEST considers each frequency to be taken from a separate dimension.[3]
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 Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1021/ac00142a008, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with
|doi=10.1021/ac00142a008instead. - ^ Attention: This template ({{cite jstor}}) is deprecated. To cite the publication identified by jstor:2685844, please use {{cite journal}} with
|jstor=2685844instead. - ^ Joseph Mendendorp and Robert A. Lodder (2006) "Acoustic-Resonance Spectrometry as a Process Analytical Technology for Rapid and Accurate Tablet Identification" AAPS PharmSciTech 7 (1) Article 25.
Further reading
- Lodder, R.; Hieftje, G. (1988). "Quantile BEAST Attacks the False-Sample Problem in Near-Infrared Reflectance Analysis". Applied Spectroscopy. 42 (8): 1351–1365.
- Y. Zou, Robert A. Lodder (1993) "An Investigation of the Performance of the Extended Quantile BEAST in High Dimensional Hyperspace", paper #885 at the Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy, Atlanta, GA
- Y. Zou, Robert A. Lodder (1993) "The Effect of Different Data Distributions on the Performance of the Extended Quantile BEAST in Pattern Recognition", paper #593 at the Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy, Atlanta, GA
 | This statistics-related article is a stub. You can help Wikipedia by expanding it. |