Collaborative Research: Statistical Analysis of Partially Observed Shapes in Two Dimensions

合作研究：二维部分观察形状的统计分析

• 批准号: 1811969
• 负责人: Ofer Harel
• 金额: $ 5万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811969&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Analysis; Partially

Classification of shapes has many important applications in a variety of fields. For example, anthropologists use shape classification on fossilized faunal teeth to reconstruct past environments and on variation in the shapes of stone tools to assess different technological strategies. Other applications include classification of plants based on the shape of their leaves and identification of shapes of tumors. While there are many statistical tools that can be used for classification, most methods are based on complete shapes with relatively few methods available for partially observed or incomplete shapes. This project focuses of the development of new statistical methodology, based on the ideas of multiple imputation, for the analysis of partially observed shapes.This project proposes as a starting point leveraging the ideas of nonparametric, hot-deck type multiple imputation to shapes that are defined by unlabeled points and/or functions, as opposed to shapes defined by landmarks where traditional methods of multiple imputation may be applied. The proposed method involves matching partially observed shapes to fully observed shapes, randomly choosing a fully observed donor shape among the shapes that are good matches for the partial shape, and then completing the partial shape with the unmatched part of the donor shape. A simulation study will be conducted to compare the relative merits of different partial matching methods. The imputation framework will be tested using teeth from the Family Bovidae, whose classification plays an important role in biological anthropology for identifying the taxa of specimens, which in turn is used to reconstruct paleoenvirnoments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

形状的分类在许多领域有着重要的应用。例如，人类学家利用化石动物牙齿的形状分类来重建过去的环境，并利用石器形状的变化来评估不同的技术策略。 其他应用包括基于叶子形状的植物分类和肿瘤形状的识别。 虽然有许多统计工具可用于分类，但大多数方法都是基于完整的形状，对于部分观察到的或不完整的形状，可用的方法相对较少。本项目的重点是开发新的统计方法，基于多重插补的思想，用于部分观察到的形状的分析。本项目的出发点是利用非参数，热甲板类型的多重插补的思想来定义由未标记的点和/或功能，而不是由地标定义的形状，其中可以应用传统的多重插补方法。 所提出的方法涉及将部分观察到的形状匹配到完全观察到的形状，在与部分形状良好匹配的形状中随机选择完全观察到的供体形状，然后用供体形状的不匹配部分完成部分形状。将进行模拟研究，以比较不同的部分匹配方法的相对优点。 估算框架将使用来自牛科的牙齿进行测试，牛科的分类在生物人类学中起着重要作用，用于识别标本的类群，进而用于重建古环境。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。