第八章。混合似然比模型法医应用:一个新的解决方案,以确定物化数据的证据价值

A. Martyna, G. Zadora
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引用次数: 0

摘要

在分析技术快速发展的时代,需要正确处理的数据量增加了。根据欧洲法医科学研究所网络,对法医应用数据的适当解释应该嵌入似然比(LR)框架中。该方法清晰地体现了法医专家在证据鉴定过程中的作用。该概念涉及在两种不利假设的背景下对证据数据进行分析,例如,从嫌疑人衣服上回收的样本和从犯罪现场收集的样本可能来自同一物体(H1),也可能不是(H2)。LR模型评估样本之间的相似性,观察其数据的频率以及总体中这些样本内部和样本之间的典型变异性,以表明哪种假设更有可能。本章重点介绍混合LR模型,该模型的开发是为了绕过对于变量多于样本的数据集训练LR模型的不可行性。它们是为化学计量学技术衍生的有限数量的变量构建的,这些变量有效地降低了数据维数,增强了训练集中样本之间的差异,并减少了它们内部的方差,从而提高了LR模型的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CHAPTER 8. Hybrid Likelihood Ratio Models for Forensic Applications: a Novel Solution to Determine the Evidential Value of Physicochemical Data
In an era of rapid advancement in analytical techniques the amount of data that needs to be properly processed increases. According to the European Network of Forensic Science Institutes, a proper interpretation of data for forensic applications should be embedded in a likelihood ratio (LR) framework. The method clearly reflects the role of the forensic expert in the process of evidence evaluation. The concept involves analysis of the evidence data in the context of two adversative hypotheses, e.g. the sample recovered from the suspect's clothing and the sample collected from the crime scene may have come from the same object (H1), or not (H2). The LR model evaluates the similarity between the samples, the frequency of observing their data and typical variability within and between such samples in the population to indicate which of the hypotheses is more likely. The chapter focuses on hybrid LR models, which were developed to bypass the infeasibility of training LR models for datasets with more variables than samples. They are constructed for a limited number of variables derived from chemometric techniques that effectively reduce data dimensionality, enhance the differences between samples in the training set and reduce the variance within them for improving the performance of LR models.
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