Conference on Nonparametric Statistics and Statistical Learning; Spring 2010; Columbus, OH

非参数统计和统计学习会议；

• 批准号: 0904165
• 负责人: Joseph Verducci
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904165&HistoricalAwards=false
• 关键词: Conference; Nonparametric; Statistics; Statistical; Learning

An international conference on Nonparametric Statistics and Statistical Learning is scheduled for May 19-22, 2010 at the Ohio State University. The conference is cosponsored by the American Statistical Association (ASA) Section on Nonparametric Statistics and the newly formed ASA Section on Statistical Learning & Data Mining. This is the first and very timely effort to explore the synergy of these two areas in a single conference. The conference features six plenary talks by internationally prominent researchers whose work relates closely to both fields. Sixteen invited breakout sessions, each with three talks, cover additional topics with potential interest to both fields. These include Robustness of Statistical Learning Methods, Implications of Data Reduction by Data Depth, Nonparametric Bayesian Methods and Model Selection, Rank Based Methods for High Dimensional Problems, Designs for Variable Screening, Ranked Set Sampling and the Collapse of Importance Sampling in Very Large Scale Systems. There are also eight contributed paper sessions and two contributed poster sessions where junior investigators and graduate students are expected to participate. Nonparametrics and Statistical Learning share key foundational structures. Both disciplines avoid unrealistic assumptions about underlying distributions or models in scientific studies; both allow for complex association among variables; and both address problems in data summarization, discovery, classification and prediction. The advent of powerful computers with accompanying massive data sets brings both disciplines to the forefront of statistical theory and practice. The goal of the proposed conference is to present some of the most important recent advances in these fields and to discuss future research directions. A major part of the conference focuses on bringing statistical research leaders together with students, postdoctoral fellows, and young academics in a stimulating environment. The funding from the NSF supports attendance of graduate students and junior researchers in American universities to present either a talk or a poster. The conference is expected to accelerate interactions and collaborations among researchers in the important areas of nonparametric statistics and statistical learning, and thereby lead to the development of new and more effective methods of modeling and inference. Statistical learning, with its roots in nonparametric statistics, pervades virtually all aspects of modern life. From automatic interpretation of hand-written numbers to sort mail by zip code, to identification of which genes to target and which chemical features might best be incorporated into new medicines, to real time analysis of satellite images in predicting hurricane paths, to anywhere else there is massive data with many sources of variation, statistical learning has become the main strategy for putting the data to good use. Additional applications include optimal allocations of credit, selection of product features to emphasize in different markets, and evaluation of concordant sources when determining the need to raise threat levels to Homeland Security. The conference features a session on national statistics with key speakers from the Bureau of Labor Statistics, the Census Bureau, and the defense industry, and Application sessions with speakers from the National Center for Atmospheric Research, the software industry, and other private sectors. Interactions among academic, governmental, and industrial statisticians in the field should strengthen both research and practice.

一个关于非参数统计和统计学习的国际会议定于2010年5月19日至22日在俄亥俄州州立大学举行。会议由美国统计协会（阿萨）非参数统计部分和新成立的阿萨统计学习数据挖掘部分共同主办。 这是在一次会议上首次非常及时地探讨这两个领域的协同作用。会议的特点是由国际知名研究人员的工作密切相关的两个领域的六个全体会议。16场特邀分组会议，每场有三场会谈，涵盖了两个领域可能感兴趣的其他主题。这些包括统计学习方法的鲁棒性，数据深度减少数据的影响，非参数贝叶斯方法和模型选择，高维问题的基于秩的方法，变量筛选设计，排序集抽样和非常大规模系统中重要性抽样的崩溃。还有八个贡献的论文会议和两个贡献的海报会议，初级研究人员和研究生预计将参加。 非参数化和统计学习共享关键的基础结构。这两个学科都避免了对科学研究中潜在分布或模型的不切实际的假设;都允许变量之间的复杂关联;都解决了数据总结，发现，分类和预测中的问题。伴随着大量数据集的强大计算机的出现，将这两个学科带到了统计理论和实践的最前沿。拟议会议的目标是介绍这些领域的一些最重要的最新进展，并讨论未来的研究方向。会议的一个主要部分侧重于将统计研究领导者与学生，博士后研究员和年轻学者聚集在一个刺激的环境中。来自NSF的资金支持美国大学的研究生和初级研究人员出席演讲或海报。预计会议将加速非参数统计和统计学习重要领域研究人员之间的互动和合作，从而导致新的和更有效的建模和推理方法的发展。 统计学习，其根源在非参数统计，几乎渗透到现代生活的各个方面。 从手写数字的自动解释到按邮政编码分类邮件，到识别哪些基因是目标基因，哪些化学特征可能最好地被纳入新药，到预测飓风路径的卫星图像的真实的时间分析，到其他任何有大量数据和许多变异来源的地方，统计学习已经成为充分利用数据的主要策略。 其他应用包括信贷的最佳分配，在不同市场中强调的产品功能的选择，以及在确定是否需要提高对国土安全的威胁级别时对一致来源的评估。会议的特点是国家统计会议与来自劳工统计局，人口普查局和国防工业的主要发言人，以及应用会议与来自国家大气研究中心，软件行业和其他私营部门的发言人。 学术界、政府和工业统计人员在该领域的互动应加强研究和实践。