报告摘要：In many applications, some covariates could be missing for various reasons. Regression quantiles could be either biased or under-powered when ignoring the missing data. Multiple imputation and EM-based augment approach have been proposed to fully utilize the data with missing covariates for quantile regression. Both methods however are computationally expensive. We propose a fast imputation algorithm (FI) to handle the missing covariates in quantile regression, which is an extension of the fractional imputation in likelihood-based regressions. FI and modified imputation algorithms (FIIPW and MIIPW) are compared to existing MI and IPW approaches in the simulation studies and applied to part of of the National Collaborative Perinatal Project study.

 报告人介绍：程豪，中国科协创新战略研究院副研究员，“科创中国”试点城市（重庆）挂点干部。毕业于中国人民大学统计学院，统计学专业博士学位。美国哥伦比亚大学、英国剑桥大学李约瑟研究所、英国伦敦政治经济学院、葡萄牙里斯本大学学院访问学者。主要研究兴趣：复杂数据分析与统计建模。主持国家自然科学基金委青年科学基金项目等。参与负责中央人才工作协调小组委托项目“国家中长期人才发展规划纲要重大人才工程实施情况总结评价”青年英才开发计划评估、国家统计局全国统计科学研究项目等。曾获中国人民大学“学术之星”、“汇丰杯”中国高校SAS数据分析大赛冠军。 在Communications in Statistics - Simulation and Computation、 Computational Statistics、Mathematics and Computers in Simulation、Communications in Statistics – Theory and Methods、数理统计与管理、统计与信息论坛、统计与决策、调研世界等国内外中英文杂志发表（含录用）论文48篇。独立翻译《统计分析：以R与Excel为分析工具》、《Python数据可视化》、《预测分析建模：Python与R语言实现》和《R统计应用开发实战》，参与编写《互联网统计》、《大数据挖掘与统计机器学习》等。

邀请人：马学俊