Collaborative Research: Estimation, Inference, and Computation for Finite Nonparametric Mixtures

协作研究：有限非参数混合物的估计、推理和计算

• 批准号: 1209007
• 负责人: David Hunter
• 金额: $ 2.35万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1209007&HistoricalAwards=false
• 关键词: Collaborative; Research; Estimation; Inference; Computation

This project aims to develop theory and methods for estimation of the functional components and weights in the nonparametric multivariate finite mixtures. These mixtures only assume that the components are drawn from some family of multivariate density functions without any parametric specification. The investigators, who are already active in this emerging area of research, adapt a number of estimation methods that are known from finite parametric mixture theory to the nonparametric context. The PI and the co-PI propose a number of practically feasible and fast algorithms that can be used to compute the resulting estimators in practice. Finally, both investigators show how to obtain large-sample asymptotic results for the proposed estimators. Finite nonparametric mixtures of distributions can provide answers to many practically important questions. As an example, they can be used to help a physician in establishing the definitive diagnosis in case of a complex medical condition with a number of possible diagnoses. An example of such a situation is a patient with a possible heart attack where other differential diagnoses are also possible. Developmental psychology provides another useful example. Indeed, study of cognitive development in children, in particular identification of strategies used by children to accomplish various tasks, can also be modeled easily using these mixtures. This has important implication for developmental psychology, providing answers to many difficult questions faced by child psychologists while helping children mature and develop in an optimal way. The PI and the co-PI propose a number of efficient algorithms to estimate these mixtures and accomplish the practical tasks mentioned above. These algorithms will be publicly available and easy to use as part of the R software package called mixtools.

本项目旨在发展非参数多元有限混合中功能分量和权重估计的理论和方法。这些混合物只假设成分是从一些多元密度函数族中提取的，没有任何参数说明。已经活跃在这一新兴研究领域的研究人员将有限参数混合理论中已知的许多估计方法应用于非参数环境。PI和co-PI提出了许多实际可行且快速的算法，可用于在实际中计算得到的估计量。最后，两位研究者展示了如何获得所提出的估计量的大样本渐近结果。有限的非参数混合分布可以为许多重要的实际问题提供答案。例如，在有多种可能诊断的复杂医疗状况的情况下，它们可用于帮助医生确定明确的诊断。这种情况的一个例子是一个可能心脏病发作的病人，其他的鉴别诊断也是可能的。发展心理学提供了另一个有用的例子。事实上，对儿童认知发展的研究，特别是对儿童完成各种任务所使用的策略的识别，也可以很容易地使用这些混合模型。这对发展心理学具有重要意义，在帮助儿童以最佳方式成熟和发展的同时，为儿童心理学家面临的许多难题提供了答案。PI和co-PI提出了许多有效的算法来估计这些混合物并完成上述实际任务。这些算法将作为R软件包mixtools的一部分公开可用并且易于使用。