Symposium on Mathematical Statistics and Applications: From Time Series and Stochastics, to Semi- and Non-Parametrics, to High-Dimensional Models

数理统计及应用研讨会：从时间序列和随机，到半参数和非参数，再到高维模型

• 批准号: 1833447
• 负责人: Frederi Viens
• 金额: $ 2.5万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1833447&HistoricalAwards=false
• 关键词: Symposium; Mathematical; Statistics; Applications; Time

This award supports participation in the conference "Symposium on Mathematical Statistics and Applications: From Time Series and Stochastics, to Semi- and Non-Parametrics, to High-Dimensional Models" held September 14 - 16, 2018 at Michigan State University. This conference is designed to benefit graduate students and early-career researchers in mathematical statistics. As the data revolution of the 21st century takes the US to new frontiers in data-based decision-making and predictive analytics in business and government, the role of the mathematically-trained and analytically-critical statistician remains more important than ever, to develop and analyze sound, innovative ways of understanding data science algorithms, methodologies, and procedures. The conference will bring together established and aspiring researchers from around the country and abroad to explore frontiers of mathematical statistics and a host of emerging applications. The exposure and networking opportunities provided by the conference to the dozens of graduate students and early-career researchers will be of great value, and potentially career-changing for some.Foundational work to further develop the analysis and use of data in all its forms is underway in several areas, particularly in semi- and non-parametric statistics. Such mathematical development is needed across a wide spectrum of the statistical sciences, including high-dimensional statistical inference, non-parametric time series, functional data analysis, empirical processes, and machine learning. The conference will expose the participants to these topics as well as other directions, including asymptotic theory of adaptive and efficient estimation, time series in econometrics, and survival analysis. With ample time built into the schedule for discussions, the conference will give participants opportunities to engage in fruitful cross-group collaborations. A particularly impactful aspect of the conference is the inclusion of several presentations by local non-statisticians who work in areas typically underserved by statisticians but with very significant data analysis needs, all interested in interacting with statisticians from across the country with whom there would ordinarily be no chance to meet. Application areas covered will include quantitative finance, insurance mathematics and risk analytics, nuclear physics, agricultural economics, neuro-imaging, soil science, hospital management, hydrology, and others. The conference website is https://stt.msu.edu/MSUStatSymposium2018/.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.

该奖项支持参加2018年9月14日至16日在密歇根州立大学举行的“数理统计与应用研讨会：从时间序列和随机，到半参数和非参数，到高维模型”。本次会议旨在使研究生和数理统计领域的早期研究人员受益。随着21世纪的数据革命将美国带到商业和政府基于数据的决策和预测分析的新前沿，受过数学训练和分析批判性的统计学家的作用比以往任何时候都更加重要，以开发和分析理解数据科学算法，方法和程序的合理，创新的方式。会议将汇集来自全国各地和国外的知名和有抱负的研究人员，探索数理统计的前沿和一系列新兴应用。会议为数十名研究生和早期职业研究人员提供了接触和交流的机会，这将是非常有价值的，对一些人来说可能会改变他们的职业生涯。在若干领域，特别是在半参数统计和非参数统计方面，正在进行进一步发展各种形式数据的分析和使用的基础工作。这样的数学发展需要跨越广泛的统计科学，包括高维统计推断、非参数时间序列、函数数据分析、经验过程和机器学习。会议将向与会者展示这些主题以及其他方向，包括自适应和有效估计的渐近理论，计量经济学中的时间序列和生存分析。会议安排了充足的讨论时间，与会者将有机会进行富有成效的跨小组合作。会议的一个特别有影响力的方面是纳入了当地非统计学家的几次演讲，他们在统计学家通常服务不足的领域工作，但有非常重要的数据分析需求，所有人都有兴趣与来自全国各地的统计学家互动，而这些统计学家通常没有机会见面。应用领域包括定量金融、保险数学和风险分析、核物理学、农业经济学、神经成像、土壤科学、医院管理、水文学等。该奖项反映了NSF的法定使命，并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，认为值得支持。