On Friday, 2021-06-18, 15:00-16:00, our institute hosted the invited talk "Multiple influential point detection in high dimensional regression spaces" by Prof. Dr. Junlong ZHAO [赵俊龙] from the School of Statistics of Beijing Normal University [北京师范大学统计学院]. We deeply thank Prof. ZHAO, which was both preceded and followed directly by another two interesting talks. His research presentation was very informative – we and the many attending students learned a lot. The discussion following the talk was also very inspiring.
|演讲题目:||Multiple influential point detection in high dimensional regression spaces|
|报告时间:||2021 年06 月18 日（星期五） 15:00-16:00|
|摘要:||Influence diagnosis is an integrated component of data analysis but has been severely under investigated in a high dimensional regression setting. One of the key challenges, even in a fixed dimensional setting, is how to deal with multiple influential points that give rise to masking and swamping effects. The paper proposes a novel group deletion procedure referred to as multiple influential point detection by studying two extreme statistics based on a marginal-correlation-based influence measure. Named the min- and max-statistics, they have complementary properties in that the max-statistic is effective for overcoming the masking effect whereas 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 a prespecified false discovery rate. The influential point detection procedure proposed is simple to implement and efficient to run and enjoys attractive theoretical properties. Its effectiveness is verified empirically via extensive simulation study and data analysis. An R package implementing the procedure is freely available.|
|简介:||赵俊龙，北京师范大学统计学院教授，主要从事高维数据分析、稳健统计，统计机 器学习等领域研究工作。在统计学各类期刊发表SCI 论文四十余篇，部分结果发表在Journal of the Royal Statistical Society: Series B（JRSSB）、 The Annals of Statistics（AOS）、Journal of American Statistical Association(JASA)，Biometrika 等统计学顶级期刊。|