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2019学术报告(7)马平教授
( 2019-07-14 )

题目:Online Sequential Leveraging Sampling Method for Streaming Data

嘉宾:马平教授(乔治亚大学)

地点:管理科研楼1018会议室

时间:2019年7月16日下午4点

摘要:Advances in data acquisition technology pose challenges in analyzing large volumes of streaming data. Sampling is a natural yet powerful tool for analyzing such data sets due to their competent estimation accuracy and low computational cost. Unfortunately, sampling methods and their statistical properties for streaming data, especially streaming time series data, are not well studied in the literature. In this talk,  I will present an online leverage-based sequential sampling algorithm for streaming time series data, which is assumed to come from an autoregressive model of order p (AR(p)). The proposed sequential leveraging sampling  (SLS) method samples only one consecutively recorded block from the data stream for inference. While the starting point of the SLS scheme is chosen using a random mechanism based on leverage scores of the data, the subsample size is decided by a sequential stopping rule. Simulation studies and real data examples will be presented to evaluate the empirical performance of the proposed SLS method. This talk is based on a joint work with Rui Xie, TN Sriram, and  Wei Biao Wu.


嘉宾简介:马平教授是美国佐治亚大学的统计学教授和大数据分析实验室的主任,美国统计协会的会士。2003年在普渡大学获得博士学位,2003年至2005年在哈佛大学从事博士后研究。2005至2013年在伊利诺伊大学香槟分校任助理和副教授。他是伊利诺伊大学高等研究中心贝克曼教授,美国国家超级计算和应用中心讲席、美国国家科学基金会CAREER AWARD获得者。他的论文获得了2011年加拿大统计杂志的最佳论文奖。他在多个国际顶尖杂志编辑委员会任副主编,包括《美国统计协会杂志》和《遗传学和分子生物学中的统计应用》。 

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