Workshop on Frontiers of Statistics: Nonparametric Modeling of Complex Data

道具ID：DMS-0531839主要研究者：范建清研究机构：普林斯顿大学研究题目：统计前沿研讨会：复杂数据的非参数建模 这是一个构思非常好的研讨会，将汇集一些统计前沿是动态和充满活力的。 本建议的目的是组织一次关于涉及大量复杂数据的统计前沿问题的讲习班。会议将重点讨论计算生物学、金融计量经济学、机器学习和工业工程等不同学科领域的数据分析非参数技术。 它还将包括统计理论和方法的各种主题。 会议的一个突出特点是，它解决了各种科学前沿出现的统计问题。本次研讨会的目的是将顶尖和初级研究人员聚集在一起，定义和扩展这些统计研究前沿。研讨会为顶尖和初级研究人员提供了一个集中的场所，以收集、交流和展示他们的新研究成果，讨论和概述各自领域的新问题，并为未来富有成效的合作奠定基础。 此外，它将提供一个概述研究生谁有兴趣在寻找一个有趣的领域工作。 该研讨会侧重于各种非参数统计问题，来自科学和工程的各个前沿，如计算生物学，金融计量经济学，机器学习和工业工程。这将对塑造解决高社会影响问题的统计研究的未来方向非常有帮助。

Prop ID: DMS-0531839 PI: Fan, Jianqing Institution: Princeton University Title: Workshop on Frontiers of Statistics: Nonparametric Modeling of Complex Data This is an extremely well conceived worshop that will bring together some of the The frontiers of statistics are dynamic and vibrant. The aim of thisproposal is to organize a workshop on the frontiers of statistics thatinvolve large and complex data. The conference will focus on data-analyticnonparametric techniques in diverse fields of disciplines such ascomputational biology, financial econometrics, machine learning, andindustrial engineering. It will also include various topics onstatistical theory and methods. A distinguished feature of the conferenceis that it addresses emerging statistical problems that arise from variousfrontiers of science. It will be very helpful for shaping the futuredirections of statistical research.The purpose of this proposal is to bring together top and juniorresearchers to define and expand these research frontiers of statistics.The workshop provides a focal venue for top and junior researchers togather, interact and present their new research findings, to discuss andoutline emerging problems in their fields, and to lay the groundwork forfruitful future collaborations. Moreover, it will provide an overview forgraduate students who are interested in seeking for an interesting area towork. The workshop focuses on various nonparametric statistical problemsfrom various frontiers of science and engineering such as computationalbiology, financial econometrics, machine learning, and industrialengineering. It will be very helpful for shaping the future directions ofstatistical research that addresses problems of high societal impact.