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Jessica Ryan-Despraz¹, Amanda Wissler²

¹Department of Physical Anthropology, University of Bern, Bern, Switzerland.

Archaeological and anthropological sciences

|October 25, 2024

概括

多种归算技术,特别是随机森林 (RF) 和多方法 (MM) 方法,在处理生物考古学混合数据集中缺少的数据方面表现出卓越的性能. 缺失的程度显著影响归算的准确性.

科学领域:

生物考古学的生物考古学
数据科学数据科学数据科学
统计建模统计建模

背景情况:

缺少数据是生物考古学研究的一个重大挑战,可能会损害分析的完整性.
推算方法为解决混合类型数据集 (定性和定量) 中缺失值的潜在解决方案.

研究的目的:

评估生物考古学中混合数据的各种归算方法的性能.
为了比较单次归算技术与适应的多次归算方法.

主要方法:

在对照数据集 (481个样本 x 41个变量) 上模拟失踪 (5-40%).
测试方法:随机森林 (RF),PCA/MCA,FAMD,热,预测平均匹配 (PMM),RSOV,以及多方法 (MM) 方法.
将单次归算与适应的多次归算方法 (R包"小鼠") 进行比较.
在三个失踪机制 (MCAR,MAR,MNAR) 中评估了性能.

主要成果:

在经过测试的方法中,自适应的多重归算始终优于单一归算.
随机森林 (RF) 和多方法 (MM) 方法显示出最佳性能.
PCA/MCA和PMM的多重归算也显示出了强的结果.
缺少数据的数量是影响归算准确性的最关键因素.

结论:

多种归算方法,特别是RF和MM,对于处理生物考古学混合数据集中缺少的数据是有效的.
虽然特定的方法优越,但归算技术的选择应该考虑数据集特征和研究目标.
推算是生物考古学研究的宝贵工具,但仔细考虑其局限性是必不可少的.

关键词:

生物考古学的生物考古学计入计算是指计入计算的方法.缺少的数据数据.混合数据是混合数据.

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