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骨测量分类中回归建模的自动化:一种排序方法。

The Automation of Regression Modeling in Osteometric Sorting: An Ordination Approach.

作者信息

Lynch Jeffrey James

机构信息

Defense POW/MIA Accounting Agency, Offutt Air Force Base, Omaha, 68113, NE.

出版信息

J Forensic Sci. 2018 May;63(3):798-804. doi: 10.1111/1556-4029.13597. Epub 2017 Jul 21.

Abstract

This study compares the original osteometric sorting association method with an ordination approach across all combinations of the humerus, ulna, radius, femur, tibia, and fibula. This includes both the original prediction interval and t-statistic approaches. Standard measurements are utilized in the models with full measurements combined and without length measurements. The sample is the osteometric sorting reference from the Defense POW/MIA Accounting Agency. A full set of performance statistics is provided. Results indicate the ordination approach outperforms the original in the majority of bone combinations. Models with length measurements have more exclusion power than those without. It is recommended for the ordination approach to supersede the original when applied to large commingled assemblages.

摘要

本研究将原始骨测量分类关联方法与一种排序方法在肱骨、尺骨、桡骨、股骨、胫骨和腓骨的所有组合上进行了比较。这包括原始预测区间和t统计量方法。在模型中使用了标准测量值,包括完整测量值的组合以及不包括长度测量值的情况。样本是来自国防战俘/失踪人员身份查验局的骨测量分类参考。提供了一整套性能统计数据。结果表明,在大多数骨骼组合中,排序方法优于原始方法。包含长度测量值的模型比不包含长度测量值的模型具有更强的排除能力。建议在应用于大型混合骨骼集合时,排序方法取代原始方法。

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