English  |   学校主页  |   收藏本站


【主 题】 Estimating Sparse Functional Additive Models

【报告人】 曹际国教授

Simon Fraser University

【时 间】 2019年1218   13:30-14:30

【地 点】 上海财经大学统计与管理学院大楼1208会议室

 We study a flexible model to address the lack of fit in conventional functional linear regression models. This model, called the sparse functional additive model, is used to characterize the relationship between a functional predictor and a scalar response of interest. The effect of the functional predictor is represented in a nonparametric additive form, where the arguments are the scaled functional principal component scores. Component selection and smoothing are considered when fitting the model in order to reduce the variability and enhance the prediction accuracy, while providing an adequate fit. To achieve these goals, we propose using the adaptive group LASSO method to select relevant components and smoothing splines and, thus, obtain a smoother estimate of those relevant components. Simulation studies show that the proposed estimation method compares favorably with conventional methods in terms of prediction accuracy and component selection. Furthermore, the advantages of our estimation method are demonstrated using two real-data examples.

嘉宾简介】曹际国博士加拿大温哥华西蒙弗雷泽大学(Simon Fraser University)统计与精算系副教授,加拿大数据科学国家特聘教授(Canada Research Chair in Data Science),国际泛华统计家协会加拿大分会执行委员,现担任四个国际优秀统计期刊(BiometricsCanadian Journal of Statistics, Journal of Agricultural, Biological, and Environmental Statistics Statistics and Probability Letters)副主编。曹际国2006年获得加拿大麦吉尔大学(McGill University) 博士,2007年美国耶鲁大学博士后出站,长期从事函数型数据分析(functional data analysis) 和估计微分方程参数的研究.曹际国2010年选为美国统计与应用数学科学学院研究员 (Fellow at Statistical and Applied Mathematical Sciences Institute), 2009年获得加拿大统计学会优秀学者奖 (AusCan Scholar, Statistical Society of Canada), 近些年来在Journal of the American Statistical Association (JASA), Journal of the Royal Statistical Society, Series B (JRSSB)Biometrics等国际统计期刊中发表超过60篇文章。