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Composite Resampling Inference for Dependent Data

基本信息

批准号：
2015390
负责人：
Daniel Nordman
金额：
$ 15万
依托单位：
Iowa State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015390&HistoricalAwards=false
关键词：
Composite Resampling Inference Dependent Data

项目摘要

Current statistical methodology for dependent data analysis often relies on specifying an adequate model, which can be difficult in practice. A potential consequence is that conclusions drawn from an inappropriate or mistaken model may be unreliable or misleading. The project seeks to develop efficient and accurate statistical methods that are "model-free" or apply without restrictive assumptions about the dependence in data. A direct benefit of this research will be to provide alternative tools for statistical inference that are not susceptible to model choice or model misspecification. Therefore, the research will benefit data-based inference in scientific areas such as environmetrics, economics, geology, and astronomy, which encounter different forms of dependent data and where model-free methods can play an important role in data analysis. The project will also support the professional development of students through graduate student mentoring as well as outreach activities with undergraduate students at local colleges and universities for promoting recruitment and education in statistics and data science.This project particularly aims to produce composite, or hybrid-type, resampling methods that combine strategies for re-using dependent data. By merging philosophically different resampling techniques (subsampling and bootstrap), the PI will investigate convolved subsampling for nonparametric inference. This new resampling method has wide applicability and favorable performance under mild conditions. For important types of strongly or long-range dependent time series, statistical inference depends heavily on an unknown process index. The PI will study resampling under long-memory and develop a first-ever estimator of this index via a composition of resampling ideas. Additionally, the PI will develop new empirical likelihood methods for time series and spatial data by combining different resampling devices (data transformations and data blocking) for inference over a variety of dependence structures.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.

目前用于相依数据分析的统计方法往往依赖于指定适当的模型，这在实践中可能很困难。一个潜在的后果是，从不适当或错误的模型得出的结论可能是不可靠的或具有误导性的。该项目寻求开发高效和准确的统计方法，这些方法是“无模型”的，或者适用于没有关于数据相关性的限制性假设。这项研究的一个直接好处将是为统计推断提供不受模型选择或模型错误指定影响的替代工具。因此，这项研究将有助于环境计量学、经济学、地质学和天文学等科学领域的基于数据的推理，这些领域遇到不同形式的相关数据，无模型方法在数据分析中可以发挥重要作用。该项目还将通过研究生辅导以及与当地大学本科生的外联活动支持学生的专业发展，以促进统计和数据科学方面的招聘和教育。该项目特别旨在产生复合或混合类型的重新采样方法，结合重复使用相关数据的战略。通过合并哲学上不同的重采样技术(次抽样和自举)，PI将研究卷积次抽样以进行非参数推断。这种新的重采样方法在温和的条件下具有广泛的适用性和良好的性能。对于重要的强相依或长相依时间序列，统计推断在很大程度上依赖于未知过程指标。PI将研究长记忆下的重采样，并通过重采样想法的组合来开发这一指数的第一个估计器。此外，PI将为时间序列和空间数据开发新的经验似然方法，通过结合不同的重采样设备(数据转换和数据块)来对各种依赖结构进行推断。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。