PDF(Pubmed)
Abstract：
It is common in forensic anthropology to draw inferences (e.g., inferences with respect to biological sex of human remains) using statistical models applied to anthropometric data. Commonly used models can output posterior probabilities, but a threshold is usually applied in order to obtain a classification. In the forensic-anthropology literature, there is some unease with this \"fall-off-the-cliff\" approach. Proposals have been made to exclude results that fall within a \"zone of uncertainty\", e.g., if the posterior probability for \"male\" is greater than 0.95 then the remains are classified as male, and if the posterior probability for \"male\" is less than 0.05 then the remains are classified as female, but if the posterior probability for \"male\" is between 0.05 and 0.95 the remains are not classified as either male or female. In the present paper, we propose what we believe is a simpler solution that is in line with interpretation of evidence in other branches of forensic science: implementation of the likelihood-ratio framework using relevant data, quantitative measurements, and statistical models. Statistical models that can implement this approach are already widely used in forensic anthropology. All that is required are minor modifications in the way those models are used and a change in the way practitioners and researchers think about the meaning of the output of those models. We explain how to calculate likelihood ratios using osteometric data and linear discriminant analysis, quadratic discriminant analysis, and logistic regression models. We also explain how to empirically validate likelihood-ratio models.
摘要：
在法医人类学中，得出推论是很常见的(例如，关于人类遗骸的生物学性别的推论）使用应用于人体测量数据的统计模型。常用的模型可以输出后验概率，但通常应用阈值以获得分类。在法医人类学文献中，这种“从悬崖上掉下来”的方法有些不安。已经提出了排除“不确定区域”内的结果的建议，例如，如果“男性”的后验概率大于0.95，那么这些遗骸被归类为男性，如果“男性”的后验概率小于0.05，那么这些遗骸被归类为女性，但是如果“男性”的后验概率在0.05到0.95之间，那么这些遗骸就不会被归类为男性或女性。在本论文中，我们提出了我们认为是一个更简单的解决方案，符合法医科学其他分支的证据解释：使用相关数据实施似然比框架，定量测量，和统计模型。可以实施这种方法的统计模型已经在法医人类学中广泛使用。所需要的只是对这些模型使用方式的微小修改，以及从业者和研究人员对这些模型输出含义的思考方式的改变。我们解释了如何使用骨测量数据和线性判别分析来计算似然比，二次判别分析，和逻辑回归模型。我们还解释了如何对似然比模型进行实证验证。
