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Competing Statistical Methods for the Fitting of Normal Species Sensitivity Distributions: Recommendations for Practitioners

机译：拟合正常物种敏感度分布的竞争统计方法：对从业者的建议

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摘要

A species sensitivity distribution (SSD) models data on toxicity of a specific toxicant to species in a defined assemblage. SSDs are typically assumed to be parametric, despite noteworthy criticism, with a standard proposal being the log-normal distribution. Recently, and confusingly, there have emerged different statistical methods in the ecotoxicological risk assessment literature, independent of the distributional assumption, for fitting SSDs to toxicity data with the overall aim of estimating the concentration of the toxicant that is hazardous to p% of the biological assemblage (usually with p small). We analyze two such estimators derived from simple linear regression applied to the ordered log-transformed toxicity data values and probit transformed rank-based plotting positions. These are compared to the more intuitive and statistically defensible confidence limit-based estimator. We conclude based on a large-scale simulation study that the latter estimator should be used in typical assessments where a pointwise value of the hazardous concentration is required.
机译：物种敏感度分布（SSD）对特定毒物对已定义组合中的物种的毒性进行数据建模。尽管值得关注，但通常假定SSD是参数化的，标准建议是对数正态分布。近年来，令人困惑的是，生态毒理风险评估文献中出现了不同的统计方法，与分布假设无关，用于使SSD适应毒性数据，其总体目标是估计对p％的生物有害的毒物浓度。组合（通常用p小）。我们分析了从简单线性回归中得出的两个这样的估计量，这些估计量适用于有序对数转换后的毒性数据值和概率转换后的基于秩的绘图位置。将这些与更直观，统计上可辩证的基于置信度限制的估计值进行比较。我们基于大规模的模拟研究得出的结论是，在需要危险浓度逐点取值的典型评估中，应使用后一种估计器。

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