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In statistics, the bootstrap error-adjusted single-sample technique (BEST or the BEAST) is a non-parametric method that is intended to allow an assessment to be made of the validity of a single sample. It is based on estimating a probability distribution representing what can be expected from valid samples. This is done use a statistical method called bootstrapping, applied to previous samples that are known to be valid. ==Methodology== BEST provides advantages over other methods such as the Mahalanobis metric, because it does not assume that for all spectral groups have equal covariances or that each group is drawn for a normally distributed population. A quantitative approach involves BEST along with a nonparametric cluster analysis algorithm. Multidimensional standard deviations (MDSs) between clusters and spectral data points are calculated, where BEST considers each frequency to be taken from a separate dimension.〔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.〕 BEST is based on a population, P, relative to some hyperspace, R, that represents the universe of possible samples. P * is the realized values of P based on a calibration set, T. T is used to find all possible variation in P. P * is bound by parameters C and B. C is the expectation value of P, written E(P), and B is a bootstrapping distribution called the Monte Carlo approximation. The standard deviation can be found using this technique. The values of B projected into hyperspace give rise to X. The hyperline from C to X gives rise to the skew adjusted standard deviation which is calculated in both directions of the hyperline.〔Sara J. Hamilton and Robert Lodder, "Hyperspectral Imaging Technology for Pharmaceutical Analysis", Society of Photo-Optical Instrumentation Engineers 〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Bootstrap error-adjusted single-sample technique」の詳細全文を読む スポンサード リンク
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