| 词条 | Bootstrap error-adjusted single-sample technique |
| 释义 |
}} 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.[1] This is done use a statistical method called bootstrapping, applied to previous samples that are known to be valid. MethodologyBEST provides advantages over other methods such as the Mahalanobis metric, because it does not assume that for all spectral groups have equal covariances {{Clarify|date=February 2011}} or that each group is drawn for a normally distributed population.[2] A quantitative approach involves BEST along with a nonparametric cluster analysis algorithm. Multidimensional standard deviations{{Clarify|date=March 2011}} (MDSs) between clusters and spectral{{Clarify|date=March 2011}} data points are calculated, where BEST considers each frequency to be taken from a separate dimension.{{Clarify|date=March 2011}}[3] 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.[4] ApplicationBEST 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] Methods such as ICP-AES require capsules{{clarify|reeason=what are capsules to do with what is going on|date=March 2011}} to be emptied for analysis. A nondestructive method is valuable. A method such as NIRA{{Clarify|date=March 2011}} can be coupled to the BEST method in the following ways.[1]
References1. ^1 2 {{Cite journal | last1 = Lodder | first1 = Robert A. | last2 = Selby | first2 = Mark. | last3 = Hieftje | first3 = Gary M. | title = Detection of capsule tampering by near-infrared reflectance analysis | doi = 10.1021/ac00142a008 | journal = Analytical Chemistry | volume = 59 | pages = 1921–1930| year = 1987 | pmid = | pmc = }} 2. ^{{Cite journal | last1 = Efron | first1 = B. | last2 = Gong | first2 = G. | title = A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation | journal = The American Statistician| volume = 37 | issue = 1 | pages = 36–48 | doi = 10.2307/2685844 | year = 1983 | pmid = | pmc = }} 3. ^Joseph Mendendorp and Robert A. Lodder (2006) "Acoustic-Resonance Spectrometry as a Process Analytical Technologyfor Rapid and Accurate Tablet Identification" AAPS PharmSciTech, 7 (1) Article 25. 4. ^Sara J. Hamilton and Robert Lodder, "Hyperspectral Imaging Technology for Pharmaceutical Analysis", Society of Photo-Optical Instrumentation Engineers {{full citation needed|date=November 2012}} Further reading
2 : Resampling (statistics)|Computational statistics |
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