Smith S L
Department of Sociology and Anthropology, University of Texas, Arlington 76019, USA.
J Forensic Sci. 1996 May;41(3):469-77.
Forensic anthropologists assign sex and population group (race) to individuals on the basis of skeletal remains. While the most useful bones for these determinations are cranial and pelvic, these are not always available. The purpose of this paper is to provide models for classification using metacarpals and hand phalanges. Four samples of 40 individuals each (black and white males and females) form the dataset. Measurements include lengths and radioulnar and dorsopalmar widths of the 19 bones of each hand. The large number of total variables necessitated separate models for metacarpal and phalangeal categories; due to the considerable number of significant differences between corresponding right and left hand variables, separate models were created for right and left sides. A stepwise discriminant procedure was used to select variables, with some highly correlated (r > 0.85) variables subsequently removed. The model for left hand metacarpals has the greatest power of discrimination (89.4%); that for right hand middle phalanges, the least (71.7%). Metacarpals assign approximately 87-89%, proximal phalanges 76-79%, middle phalanges 72-79%, and distal phalanges 81-83% of individuals to their correct sex and population groups. Models exchanging variables selected from one side for corresponding variables on the other show discriminating power ranging from 72.3 to 85.6%. Thus roughly 70-90% of individuals are correctly classified by these models; more conservative "jackknife" estimates yield a success rate of approximately 67-82%. When these models are used for classification of sex alone, 89.9-94.4% ("jackknife" range, 88.7%-94.4%) of cases are correctly classified; for race alone, 80.5-98.1% ("jackknife" range, 77.4-96.9%).
法医人类学家根据骨骼遗骸来确定个体的性别和人群类别(种族)。虽然用于这些判定的最有用的骨骼是颅骨和骨盆,但这些骨骼并非总是可得的。本文的目的是提供使用掌骨和指骨进行分类的模型。四个样本,每个样本有40个人(黑人与白人男性和女性)构成了数据集。测量包括每只手19块骨头的长度以及桡尺宽度和背掌宽度。由于总变量数量众多,需要为掌骨和指骨类别分别建立模型;由于相应的右手和左手变量之间存在大量显著差异,因此为右侧和左侧分别创建了模型。采用逐步判别程序来选择变量,随后去除了一些高度相关(r > 0.85)的变量。左手掌骨模型的判别能力最强(89.4%);右手中间指骨模型的判别能力最弱(71.7%)。掌骨将大约87 - 89%的个体、近端指骨将76 - 79%的个体、中间指骨将72 - 79%的个体以及远端指骨将81 - 83%的个体正确归类到其相应的性别和人群类别。用从一侧选择的变量替换另一侧相应变量的模型,其判别能力在72.3%至85.6%之间。因此,这些模型大约能正确分类70 - 90%的个体;更保守的“留一法”估计得出的成功率约为67 - 82%。当这些模型仅用于性别分类时,89.9 - 94.4%(“留一法”范围为88.7% - 94.4%)的案例能被正确分类;仅用于种族分类时,80.5 - 98.1%(“留一法”范围为77.4 - 96.9%)的案例能被正确分类。