Modeling, Computational and Inferential Issues in Fingerprint and Health Monitoring Applications

指纹和健康监测应用中的建模、计算和推理问题

• 批准号: 1106450
• 负责人: Chae Young Lim
• 金额: $ 16.95万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1106450&HistoricalAwards=false
• 关键词: Modeling; Computational; Inferential; Issues; Fingerprint

Complex data structures with intrinsic multivariate and multilevel characteristics arise frequently in various scientific disciplines. This proposal discusses two such application domains, namely, the areas of health monitoring and fingerprint based authentication. The scientific questions posed are usually associated with high-dimensional parameter spaces, such as spaces of point patterns and functions, and are addressed in a conceptually unified and meaningful way with the development of novel hierarchical models on general object spaces. These hierarchical models are flexible and adept at capturing salient data characteristics, such as clustering and spatial dependence, whose forms vary from one application to another. The proposal develops statistical methodology utilizing parametric multivariate generalized linear mixed models and non-parametric Dirichlet process priors, extended to the space of objects, for studying attributes and the effect of covariates encountered in fingerprint and socio-economic-health applications. Due to the high-dimensionality of the intrinsic spaces, several innovative procedures are developed to overcome ensuing computational challenges in the Bayesian framework, including theoretically justified approximations to the likelihood and predictive inference. An added feature of these inferential tools is to extend posterior analysis of quantities such as means, variances and credible sets, in a meaningful way to the space of objects. The scientific goals addressed in this proposal will benefit research in public and social health, engineering and legal forensics. The impact on societal and demographic policy making, for example, will be in the discovery of socio-demographic regions with extreme health and economic conditions, the identification of their potential causes and in the decisions made to mobilize resources accordingly. The proposed research has impact on how forensic evidence should be reported as well. Many forensic scientists as well as legal scholars have become increasingly aware of the shortcomings of fingerprint evidence as is presented in a court of law, and that methodology for assessing the extent of uniqueness of fingerprints requires further scientific validity. Several of these issues are addressed by developing quantitative methods for reporting fingerprint evidence, for example, when additional fingerprint attributes such as quality are available. This research will have broader impact in health and security surveillance, and their monitoring.

具有内在的多变量和多层次特征的复杂数据结构经常出现在各个科学学科中。该提案讨论了两个这样的应用领域，即健康监控和基于指纹的身份验证领域。提出的科学问题通常与高维参数空间相关，例如点模式和函数的空间，并且随着一般对象空间上新颖的层次模型的发展，以概念上统一且有意义的方式得到解决。这些分层模型灵活且善于捕获显着的数据特征，例如聚类和空间依赖性，其形式因应用程序而异。该提案利用参数多元广义线性混合模型和非参数狄利克雷过程先验开发统计方法，扩展到对象空间，用于研究指纹和社会经济健康应用中遇到的属性和协变量的影响。由于内在空间的高维性，开发了几种创新程序来克服贝叶斯框架中随之而来的计算挑战，包括理论上合理的似然近似和预测推理。这些推理工具的一个附加功能是以有意义的方式将均值、方差和可信集等数量的后验分析扩展到对象空间。 该提案中提出的科学目标将有利于公共和社会健康、工程和法律取证方面的研究。例如，对社会和人口政策制定的影响将体现在发现健康和经济条件极端的社会人口区域、查明其潜在原因以及相应地调动资源的决策中。拟议的研究也对如何报告法医证据产生影响。许多法医科学家和法律学者越来越意识到在法庭上提出的指纹证据的缺点，并且评估指纹唯一性程度的方法需要进一步的科学有效性。其中一些问题可以通过开发报告指纹证据的定量方法来解决，例如，当额外的指纹属性（例如质量）可用时。这项研究将对健康和安全监测及其监测产生更广泛的影响。