Statistical estimation of analytical data distributions and censored measurements.
Statistical estimation of analytical data distributions and censored measurements.
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DOI：
10.1021/ac00199a008
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发表时间：
1989-12
影响因子：
7.4
通讯作者：
K. Nielson;V. Rogers
中科院分区：
文献类型：
--
作者：
K. Nielson;V. Rogers
提出了一种估计分析数据未知分布形状和估计截尾数据点期望值的数值方法。该方法是基于概念上的正常概率图。对数据进行排序，然后使用幂函数进行变换，以相对于计算出的正态累积概率尺度实现近似线性。幂变换中使用的指数是分布形状的指数，其覆盖正态性定义为d = 1且对数正态性定义为d = 0的连续体。删失点的预期转换值是根据拟合到转换后的可接受数据的直线计算的，然后将其转换回原始分布。该方法给出了改进的分析数据分布的表征，特别是在分布极值。它还避免了由于分析检测限附近的测量所产生的删失数据处理不当而产生的偏倚。说明性的应用计算大气中的SO2数据和汉堡包中的矿物质浓度。
A numerical method was developed for estimating the shapes of unknown distributions of analytical data and for estimating the expected values of censored data points. The method is based conceptually on the normal probability plot. Data are ordered and then transformed by using a power function to achieve approximate linearity with respect to a computed normal cumulative probability scale. The exponent used in the power transformation is an index of the distribution shape, which covers a continuum on which normality is defined as d = 1 and log normality is defined as d = 0. Expected transformed values of censored points are computed from a straight line fitted to the transformed, accepted data, and these are then back-transformed to the original distribution. The method gives improved characterization of analytical data distributions, particularly in the distribution extremities. It also avoids the biases from improper handling of censored data arising from measurements near the analytical detection limit. Illustrative applications were computed for atmospheric SO2 data and for mineral concentrations in hamburgers.