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马平教授学术报告:Asympirical Analysis: Theory Informs Practice


报告题目:Asympirical Analysis: Theory Informs Practice

报 告 人:马平教授(美国佐治亚大学统计系)

报告时间:2019年7月1日(周一) 10:00

报告地点:知新楼B-1238


报告摘要:

Large samples have been generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyze such large samples due to expensive computational costs. In particular, the daunting computational costs of selecting smoothing parameters render the smoothing spline ANOVA models impractical. In this talk, I will present an asympirical (asymptotic + empirical) smoothing parameters selection approach for smoothing spline ANOVA models in large samples. The proposed method can significantly reduce computational costs of selecting smoothing parameters in high-dimensional and large-scale data. We show smoothing parameters chosen by the proposed method tend to the optimal smoothing parameters minimizing a risk function. In addition, the estimator based on the proposed smoothing parameters achieves the optimal convergence rate. Extensive simulation studies will be presented to demonstrate numerical advantages of our method over competing methods. I will further illustrate the empirical performance of the proposed approach using two real data examples.


报告人简介:

马平教授是美国佐治亚大学统计学教授和大数据分析实验室主任。2003年,他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年加拿大统计杂志最佳论文奖。他在多个国际顶尖杂志编辑委员会任副主编,其中包括《美国统计协会杂志》和《遗传学和分子生物学中的统计应用》杂志。他还是美国统计协会的会士。


欢迎各位老师同学积极参加!