The estimation of the sex of the individual is a parameter of great value in forensic contexts and, above all, in archaeological contexts, where it is more difficult to apply genetic studies. In contrast with methods based on non-metric variables, we propose the use of a random generalized linear model for the determination of sex, starting from the Howells craniometric database and testing them on the dataset of known sex of the Forensic Data Bank, with 2524 and 1314 individuals respectively. After eliminating the individual’s considered outliers or with missing data, we proceeded to analyse which variables were more dimorphic between sexes (bizigomatic width, ZYB; bijugal width, JUB; mastoid height, MDH; glabela-occipital length, GOL; bifrontal width, FMB); these were used to build the statistical model. Subsequently, a comparison was made between the functions proposed by other authors and our model to determine their capacity in absolute terms, as well as by sex. The result is a random generalized linear model made up of 300 bags that, based on the five measures mentioned, reached 86.26% precision classifying the sex of individuals from the Forensic Data Bank (89.7% in the male sample and 82.82% in the female one). Although the method presented here should be taken with caution and not as the only way to estimate sex, it has proven to be statistically accurate in addition to having a non-regional vocation.

中文翻译：

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基于颅骨测量的性别随机确定的新型广义线性模型

在法庭环境中，尤其是在考古环境中，对个人性别的估计是非常有价值的参数，因为在考古环境中，应用基因研究更加困难。与基于非度量变量的方法相比，我们建议使用随机广义线性模型来确定性别，该方法从Howells颅骨数据库开始，并在2524年的法医数据库已知性别的数据集中进行测试和1314个人。在消除了个体认为的异常值或缺少数据之后，我们继续分析哪些变量在性别之间更为双态（bizigomatic宽度，ZYB； bijugal宽度，JUB；乳突高度，MDH； glabela-枕骨长度，GOL；双额叶宽度，FMB） ; 这些被用来建立统计模型。后来，在其他作者提出的功能与我们的模型之间进行了比较，以确定其绝对值和性别的功能。结果是一个由300个袋子组成的随机广义线性模型，根据提到的五种方法，对法医学数据库中的性别进行分类的准确度达到了86.26％（男性样品为89.7％，女性样品为82.82％ ）。尽管此处介绍的方法应谨慎使用，而不是估计性别的唯一方法，但除具有非区域性职业外，事实证明该方法在统计上也是准确的。来自法证数据库的个人性别分类准确率达到26％（男性样本中89.7％，女性样本中82.82％）。尽管此处介绍的方法应谨慎使用，而不是估计性别的唯一方法，但除具有非区域性职业外，事实证明该方法在统计上也是准确的。来自法证数据库的个人性别分类准确率达到26％（男性样本中89.7％，女性样本中82.82％）。尽管此处介绍的方法应谨慎使用，而不是估计性别的唯一方法，但除具有非区域性职业外，事实证明该方法在统计上也是准确的。