报告时间：2018年10月10日 15:00-16:00
报告地点：明德主楼1016会议室
报告主题：Multiple Influential Point Detection in High-Dimensional Spaces
报告摘要：
Influence diagnosis should be routinely conducted when one aims to construct a regression model. Despite its importance, the problem of influence quantification is severely under-investigated in a high dimensional setting, mainly due to the difficulty of establishing a coherent theoretical framework and the lack of easily implementable procedures. Although some progress has been made in recent years, existing approaches are ineffective in detecting multiple influential points due to the notorious “masking” and “swamping” effects. To address this challenge, we propose a new group deletion procedure referred to as MIP by introducing two novel quantities named Max and Min statistics. These two statistics have complimentary properties in that the Max statistic is effective for overcoming the masking effect while the Min statistic is useful for overcoming the swamping effect. Combining their strengths, we further propose an efficient algorithm that can detect influential points with prespecified guarantees. For wider applications, we focus on developing the new proposal for the multiple response regression model, encompassing the univariate response linear model as a special case. The proposed influential point detection procedure is simple to implement, efficient to run, and enjoys attractive theoretical properties. Its effectiveness is verified empirically via extensive simulation study and data analysis.
个人简介：赵俊龙，北京师范大学副教授，博导。
研究领域：高维数据分析，统计学习理论。
在The Annals of Statistics, Statistic Sinica, Bernoulli, Statistics and Computing, Computations Statistic& Data Analysis等统计学著名期刊发表论文近三十余篇。主持完成多项国家自然科学基金项目。