Evaluation of trace evidence in the form of multivariate data
Evaluation of trace evidence in the form of multivariate data
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DOI：
10.1046/j.0035-9254.2003.05271.x
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发表时间：
2004-01-01
影响因子：
1.6
通讯作者：
Lucy, D
中科院分区：
文献类型：
--
作者：
Aitken, CGG;Lucy, D
对犯罪现场和犯罪嫌疑人身上的微量物证特征进行测量评价是法医学的一个重要组成部分。描述了多元数据证据价值的五种评估方法。两个是基于显着性检验和三个似然比的评价。似然比是证据价值的一个有据可查的衡量标准，它将假设犯罪现场和可疑证据有共同来源的证据的测量结果的概率与假设犯罪现场和可疑证据有不同来源的证据的测量结果的概率进行比较。其中一种似然比方法将数据转换为基于第一主成分的单变量投影。多元数据的似然比的另外两个版本解释了变量之间的相关性和两个水平的变异：源之间的变异和源内部的变异。一个版本假设源间变异性由多元正态分布建模;另一个版本用多元核密度估计建模变异性。结果进行了比较，从玻璃的元素组成的测量分析。
The evaluation of measurements on characteristics of trace evidence found at a crime scene and on a suspect is an important part of forensic science. Five methods of assessment for the value of the evidence for multivariate data are described. Two are based on significance tests and three on the evaluation of likelihood ratios. The likelihood ratio which compares the probability of the measurements on the evidence assuming a common Source for the crime scene and suspect evidence with the probability of the measurements on the evidence assuming different sources for the crime scene and suspect evidence is a well-documented measure of the value of the evidence. One of the likelihood ratio approaches transforms the data to a univariate projection based on the first principal component. The other two versions of the likelihood ratio for multivariate data account for correlation among the variables and for two levels of variation: that between sources and that within sources. One version assumes that between-source variability is modelled by a multivariate normal distribution; the other Version models the variability with a multivariate kernel density estimate. Results are compared from the analysis of measurements on the elemental composition of glass.