全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于多元玻璃物证的非平衡似然比比对检验模型建立及评估
Establishment and Evaluation of Non-Equilibrium Likelihood Ratio Comparison Test Model Based on Multi-Element Glass Physical Evidence

DOI: 10.12677/aam.2025.145238, PP. 100-109

Keywords: 法医科学,多元数据,似然比,图形模型,两类错误率
Forensic Science
, Multivariate Data, Likelihood Ratio, Graphical Models, Two Types of Error Rates

Full-Text   Cite this paper   Add to My Lib

Abstract:

对在犯罪现场和嫌疑人身上发现的证据特征进行测量和评估是刑事调查中的重要部分。Aitken等在2006年提出了样本平衡的多元正态分布三层次证据似然比模型。基于玻璃数据,本文考虑了一种非平衡类型的多元正态三层次证据:包含7种元素的玻璃碎片数据58组,其中每组包含的玻璃样本数量不同(样本非平衡性);共有396个玻璃样本,每个样本都被测量4次。为降低数据维度,利用图形模型(graphical models)找出具有高度相关的变量。假定数据服从多元正态分布,建立样本非平衡的多元正态分布三层次证据似然比模型。在58对同一来源对比中, LR>1 的有20对,假阴性率约为34.48%;在1653对不同来源对比中, LR>1 的有78对,假阳性率约为4.7%。对于司法机关来说,评价的假阳性率更为重要,因为假阳性结果是对应于对无辜者的证据。本文建立的基于图形模型的非平衡似然比建模为司法科学中解决非平衡数据提供了一种新的方案。
In criminal investigation, the measurement and evaluation of the characteristics of the evidence found at the crime scene and on the suspect is an important part. Aitken et al. proposed a multivariate normal distribution three-level evidence likelihood ratio model with sample balance in 2006. Based on the glass data, we consider a non-equilibrium type of multivariate normal three-level evidence: 58 sets of glass fragment data containing 7 elements, each of which contains a different number of glass samples (sample non-equilibrium). There were 396 glass samples, each of which was measured four times. To reduce the dimensionality of the data, graphical models are used to find variables with high correlation. In the 58 pairs from the same source, there were 20 pairs LR>1 , and the false negative rate was about 34.48%. In the comparison of 1653 pairs from different sources, there were 78 pairs LR>1 , and the false positive rate was about 4.7%. For the prosecution, the false positive rate of the evaluation is more important because false positive results correspond to evidence against an innocent person. The non-equilibrium likelihood ratio modeling based on graphical model constructed in this paper provides a new scheme for solving non-equilibrium data in forensic science.

References

[1]  王桂强. 物证鉴定范式发展(二): 贝叶斯似然比框架[J]. 刑事技术, 2024, 49(5): 441-455.
[2]  Bozza, S., Franco, T. and Alex, B. (2022) Bayes Factors for Forensic Decision Analyses with R. Springer Nature, 2-5.
[3]  Menżyk, A., Martyna, A., Damin, A., Vincenti, M. and Zadora, G. (2023) Breaking with Trends in Forensic Dating: A Likelihood Ratio-Based Comparison Approach. Forensic Science International, 349, Article ID: 111763.
https://doi.org/10.1016/j.forsciint.2023.111763
[4]  Falcone, R., Sommariva, G. and Verità, M. (2006) WDXRF, EPMA and SEM/EDX Quantitative Chemical Analyses of Small Glass Samples. Microchimica Acta, 155, 137-140.
https://doi.org/10.1007/s00604-006-0531-z
[5]  Guo, H., Hu, C., Wang, P., Mei, H., Li, Y. and Zhu, J. (2023) Application of Lead Isotope Signature and Likelihood Ratio Evaluation in a Shooting Incident Investigation. Forensic Science International, 351, Article ID: 111812.
https://doi.org/10.1016/j.forsciint.2023.111812
[6]  Martyna, A., Lucy, D., Zadora, G., Trzcinska, B.M., Ramos, D. and Parczewski, A. (2013) The Evidential Value of Microspectrophotometry Measurements Made for Pen Inks. Analytical Methods, 5, 6788-6795.
https://doi.org/10.1039/c3ay41622d
[7]  Klemenc, S. (2001) In Common Batch Searching of Illicit Heroin Samples—Evaluation of Data by Chemometrics Methods. Forensic Science International, 115, 43-52.
https://doi.org/10.1016/s0379-0738(00)00306-6
[8]  郭洪玲, 王萍, 胡灿, 等. 基于折射率数据的玻璃物证比对检验似然比模型建立及评估[J]. 刑事技术, 2023, 48(4): 355-363.
[9]  Aitken, C.G.G., Lucy, D., Zadora, G. and Curran, J.M. (2006) Evaluation of Transfer Evidence for Three-Level Multivariate Data with the Use of Graphical Models. Computational Statistics & Data Analysis, 50, 2571-2588.
https://doi.org/10.1016/j.csda.2005.04.005
[10]  Taroni, F., Bozza, S., Biedermann, A., Garbolino, P. and Aitken, C. (2010) Data Analysis in Forensic Science: A Bayesian Decision Perspective. Wiley, 323-340.
https://doi.org/10.1002/9780470665084
[11]  Zadora, G., Martyna, A., Ramos, D. and Aitken, C. (2013) Statistical Analysis in Forensic Science: Evidential Value of Multivariate Physicochemical Data. Wiley, 187-188.
https://doi.org/10.1002/9781118763155
[12]  Curran, J.M., Triggs, C.M., Almirall, J.R., Buckleton, J.S. and Walsh, K.A.J. (1997) The Interpretation of Elemental Composition Measurements from Forensic Glass Evidence: II. Science & Justice, 37, 245-249.
https://doi.org/10.1016/s1355-0306(97)72198-1
[13]  Campbell, G.P. and Curran, J.M. (2009) The Interpretation of Elemental Composition Measurements from Forensic Glass Evidence III. Science & Justice, 49, 2-7.
https://doi.org/10.1016/j.scijus.2008.09.001
[14]  赵军圣, 庄光明, 王增桂. 极大似然估计方法介绍[J]. 长春理工大学学报, 2010, 5(6): 53-54.
[15]  Zadora, G., Neocleous, T. and Aitken, C. (2010) A Two‐Level Model for Evidence Evaluation in the Presence of Zeros. Journal of Forensic Sciences, 55, 371-384.
https://doi.org/10.1111/j.1556-4029.2009.01316.x
[16]  Zadora, G., Martyna, A., Ramos, D. and Aitken, C. (2013) Statistical Analysis in Forensic Science: Evidential Value of Multivariate Physicochemical Data. Wiley, 99-105.
https://doi.org/10.1002/9781118763155
[17]  Hayes, J.F. and Hill, W.G. (1980) A Reparameterization of a Genetic Selection Index to Locate Its Sampling Properties. Biometrics, 36, 237-248.
https://doi.org/10.2307/2529975
[18]  Aitken, C., Roberts, P. and Jackson, G. (2010) Communicating and Interpreting Statistical Evidence in the Administration of Criminal Justice. Practitioner Guide No. 1: Fundamentals of Probability and Statistical Evidence in Criminal Proceedings. Royal Statistical Society.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133