Exposure estimation in the presence of nondetectable values: Another look
Exposure estimation in the presence of nondetectable values: Another look
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DOI：
10.1080/15298660108984622
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发表时间：
2001-03-01
期刊：
影响因子：
--
通讯作者：
Verma, DK
中科院分区：
文献类型：
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作者：
Finkelstein, MM;Verma, DK
工业卫生员面临的一个常见问题是选择一种有效的方法来处理那些据报道含有无法检测到的污染物的样本。1990年，Hornung和Reed比较了一种极大似然估计(MLE)统计方法和两种涉及检测限的方法L。MLE方法被证明在各种条件下都能产生均值和标准差的无偏估计。然而，这种方法很复杂，需要进行困难的数学计算。两个较简单的替代方案涉及用L/2或L/根2来代替每个不可检测的值。当数据不是高度偏态时，推荐使用L/ROOT2方法。虽然最大似然估计方法对工业卫生数据集的平均值和标准偏差的估计值低于检测下限，但在1990年推荐这种方法是不现实的。然而，随着过去十年桌面计算的进步，最大似然估计方法现在很容易在常见的电子表格软件中实现。本文将演示如何使用电子表格软件实现此方法。
A common problem faced by industrial hygienists is the selection of a valid way of dealing with those samples reported to contain nondetectable values of the contaminant. In 1990, Hornung and Reed compared a maximum likelihood estimation (MLE) statistical method and two methods involving the limit of detection, L. The MLE method was shown to produce unbiased estimates of both the mean and standard deviation under a variety of conditions. That method, however, was complicated, requiring difficult mathematical calculations. Two simpler alternatives involved the substitution of L/2 or L/root2 for each nondetectable value. The L/root2 method was recommended when the data were not highly skewed. Although the MLE method produces the best estimates of the mean and standard deviation of an industrial hygiene data set containing values below the detection limit, it was not practical to recommend this method in 1990.However, with advances in desktop computing in the past decade the MLE method is now easily implemented in commonly available spreadsheet software. This article demonstrates how this method may be implemented using spreadsheet software.