CAREER: Locally Adaptive Nonparametric Estimation for the Modern Age - New Insights, Extensions, and Inference Tools

职业：现代局部自适应非参数估计 - 新见解、扩展和推理工具

• 批准号: 1554123
• 负责人: Ryan Tibshirani
• 金额: $ 40万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1554123&HistoricalAwards=false
• 关键词: CAREER; Locally; Adaptive; Nonparametric; Estimation

Nonparametric modeling---which means, roughly, flexible modeling of smooth trends without specific assumptions about their form or shape---finds diverse applications in many areas such as epidemiology, astrophysics, finance, and artificial intelligence. It is also a field ripe for modern statistical development, since nonparametric models are in a sense even more appealing in the "big data" era, as it is precisely in data-rich settings that the increased flexibility of these models will begin to show real rewards in terms of statistical accuracy. The proposed work will develop nonparametric methods (and affiliated software) that will be useful to data scientists who model smooth, nonlinear trends in areas like those mentioned above, as well as many others. A specific scientific emphasis will be the forecasting of influenza and dengue fever. Such forecasts will help policy makers design and implement more effective countermeasures towards these diseases. The proposal puts forward two main ideas for educational training, closely related to the research aims to be pursued. The first is a set of short videos on nonparametric smoothing, intended as supplements to an undergraduate level course called Advanced Methods for Data Analysis. They will be integrated with an interactive quiz system, and will be made freely available (on YouTube) so that others outside the class may watch too. The second idea is a statistical computation training group, for PhD students from Statistics and Computer Science."Locally adaptive" nonparametric methods offer more fine-grained flexibility than traditional nonparametric methods, in that they can simultaneously represent different amounts of smoothness at different parts of the function domain. Currently, locally adaptive nonparametric methods are not often used in big, modern data sets, likely because of their computational inefficiency, and the general inavailability of locally adaptive methods in many modern problem settings. The proposed work seeks to change this, and to push the state of the art in modern locally adaptive nonparametric estimation. The research aims are to: deepen the theoretical understanding of existing locally adaptive methods for univariate problems; efficiently scale these methods and extend these theories to problems where data are collected in high dimensions and over graphs; and develop inferential tools for all of these locally adaptive procedures. The specific contributions will be balanced between the theoretical (statistical theories that describe the underpinnings of the methods in question) and computational (practical algorithms that describe implementation of these methods at scale) perspectives. A final more applied research aim is to use the proposed methods to improve and extend a forecasting system for major epidemics such as influenza and dengue fever.

非参数建模--粗略地说，这意味着对平滑趋势的灵活建模，而不需要对其形式或形状进行特定的假设--在流行病学，天体物理学，金融和人工智能等许多领域都有不同的应用。这也是一个适合现代统计发展的领域，因为在“大数据”时代，非参数模型在某种意义上甚至更有吸引力，因为正是在数据丰富的环境中，这些模型灵活性的增加将开始显示出统计准确性方面的真实的回报。拟议的工作将开发非参数方法（和附属软件），这将有助于数据科学家在上述领域以及许多其他领域对平滑，非线性趋势进行建模。一个具体的科学重点将是流感和登革热的预测。这种预测将有助于决策者设计和实施更有效的防治这些疾病的对策。该提案提出了两个与研究目标密切相关的教育培训的主要想法。第一个是一组关于非参数平滑的短视频，旨在作为本科课程的补充，称为数据分析的高级方法。它们将与互动问答系统相结合，并将免费提供（在YouTube上），以便课堂外的其他人也可以观看。第二个想法是为统计学和计算机科学的博士生建立一个统计计算培训小组。“局部自适应”非参数方法比传统的非参数方法提供了更细粒度的灵活性，因为它们可以同时表示函数域不同部分的不同平滑度。目前，局部自适应非参数方法不常用于大型现代数据集，可能是因为它们的计算效率低下，以及在许多现代问题设置中局部自适应方法的普遍不可用性。拟议的工作旨在改变这一点，并推动现代局部自适应非参数估计的最新技术。研究的目的是：深化对单变量问题的现有局部自适应方法的理论理解;有效地扩展这些方法，并将这些理论扩展到高维和超图数据收集的问题;并为所有这些局部自适应程序开发推理工具。具体的贡献将在理论（描述相关方法基础的统计理论）和计算（描述这些方法大规模实施的实用算法）视角之间进行平衡。最后一个更实用的研究目标是使用所提出的方法来改进和扩展流感和登革热等重大流行病的预测系统。