Conference on resampling methods and high dimensional data

该项目将于2010年3月25日至26日在德克萨斯州大学城的德州农工大学组织一个为期两天的国际会议，名为“重采样方法和高维数据会议”。会议提供了一个独特的平台，汇集了目前统计研究的两个前沿领域的研究人员，即复杂数据结构的重采样方法和高维数据的推理，以促进这两个主题的研究思想交流，并进一步发展这些蓬勃发展的领域。本次会议由美国国家统计科学研究所和美国统计协会非参数统计分会共同主办。会议有一个由S.N. Lahiri教授（德克萨斯农工大学）主持的国际项目委员会，以及一个由R.J. Carroll教授（德克萨斯农工大学）、Bradley Efron教授（斯坦福大学）和Hans R. K¨unsch教授（瑞士苏黎世联邦理工学院）组成的咨询委员会。为期两天的重采样方法和高维数据会议汇集了顶级和初级研究人员，以定义和扩展处理复杂数据结构的统计和概率两个高度活跃的研究领域的研究前沿。有许多科学和工程领域，如生物信息学、脑成像、计量经济学、电气工程、金融、气象学等，这些数据经常出现，并直接受益于这些主题的进展。该基金为研究生、少数民族和青年研究人员等不能参加会议的特定参与者群体提供支持，从而促进人力资源开发。

This project focuses on organizing a 2-day international conference entitled Conference on resampling methods and high dimensional data at the Texas A & M university, College Station, TX, March 25-26, 2010. The conference provides a unique platform for bringing together researchers working in two cutting edge areas of current research in statistics, namely, Resampling methods for complex data structures and Inference for high dimensional data, in order to facilitate the exchange of research ideas on the two topics and to further the development of these booming fields. The meeting is co-sponsored by the National Institute of Statistical Sciences and by the Section on Nonparametric Statistics, American Statistical Association. The conference has an international program committee chaired by Professors S.N. Lahiri (Texas A & M), and an Advisory Committee, consisting of Professors R.J. Carroll (Texas A & M), Bradley Efron (Stanford), and Hans R. K¨unsch (ETH, Zurich, Switzerland).The two-day conference Conference on resampling methods and high dimensional data brings together top and junior researchers to define and expand the research frontiers of two highly active research areas of statistics and probability that deal with complex data structures. There are a number of areas of science and engineering, such as bio-informatics, brain imaging, econometrics,electrical engineering, finance, meteorology, etc., where such data appear frequently and which directly benefit from advances in these topics. The funding provides support for selected groups of participants including graduate students, minorities, and young researchers who can not attend the conference otherwise, thereby promoting human resource development.