University of Illinois at Urbana-Champaign, Department of Anthropology, 109 Davenport Hall, 607 S. Matthews Avenue, Urbana, IL 61801, United States.
Forensic Sci Int. 2022 Jan;330:111135. doi: 10.1016/j.forsciint.2021.111135. Epub 2021 Dec 2.
When estimating the age of an individual it is critical that 1) age ranges are as narrow as possible while still capturing the true age of the individual with an acceptable frequency, and 2) this frequency is known. When multiple traits are used to produce a single age estimate, the simplest practice is to assume that the traits are conditionally independent from one another given age. Unfortunately, if the traits are correlated once the effect of age is accounted for, the resulting age intervals will be too narrow. The frequency at which the age interval captures the true age of the individual will be decreased below the expected value to some unknown degree. It is therefore critical that age estimation methods that include multiple traits incorporate the possible correlations between them. Moorrees et al. (1963) [1] scores of the permanent mandibular dentition from 2607 individuals between 2 and 23 years were used to produce and cross-validate a cumulative probit model for age estimation with an optimal number of stages for each tooth. Two correction methods for covariance of development between teeth were tested: the variance-covariance matrix for a multivariate normal, and the Boldsen et al. (2002) [2] ad-hoc method. Both correction methods successfully decreased age interval error rates from 21% to 23% in the uncorrected model to the expected value of 5%. These results demonstrate both the efficacy of these correction methods and the need to move away from assuming conditional independence in multi-trait age estimation.
在估计个体的年龄时,有两个关键问题:1)年龄范围尽可能狭窄,同时仍能以可接受的频率捕捉到个体的真实年龄;2)要知道这个频率。当使用多个特征来生成单个年龄估计值时,最简单的做法是假设这些特征在给定年龄的情况下彼此条件独立。不幸的是,如果在考虑年龄影响后这些特征仍然相关,那么生成的年龄区间将过于狭窄。该年龄区间捕获个体真实年龄的频率将低于预期值,其降低程度未知。因此,包含多个特征的年龄估计方法必须考虑它们之间可能存在的相关性。Moorrees 等人(1963)[1]使用了 2607 名 2 至 23 岁个体的下颌永久牙齿评分,生成并交叉验证了一个具有最佳阶段数的累积概率比模型,用于年龄估计。测试了两种用于牙齿间发育协方差校正的方法:多元正态分布的方差协方差矩阵和 Boldsen 等人(2002)[2]的特定方法。这两种校正方法都成功地将未经校正模型中年龄区间误差率从 21%降至 23%,达到了 5%的预期值。这些结果表明,这些校正方法既有效,又需要避免在多特征年龄估计中假设条件独立性。