报告题目：Clusterwise functional linear regression models。
报告人: 李婷（复旦大学）
报告时间：2020年1月6日下午3:00-5:00.
报告地点：数理楼小报告厅145.
报告摘要：This paper studies clusterwise functional linear regression models with multiple functional predictors, which allow heterogeneous variation of regression patterns for different subgroups of subjects. We estimate the coefficients  based on the M-estimation with respect to possible groupings of subjects. Fast and efficient computation can be obtained by using the functional principal component basis. A Bayesian information criterion is proposed to select the unknown number of groups and shown to be consistent in model selection. We also obtain the convergence rate of the set of estimators to the set of true coefficients for all clusters. Simulation studies and a real data analysis to the Alzheimer's Disease Neuroimaging Initiative study are conducted to evaluate the finite-sample performance and practicality of the proposed method.
简介：李婷，复旦大学管理学院概率论与数理统计专业博士，2017-2018年作为联合培养博士生在美国德州大学MD安德森癌症中心学习，2019年7-9月访问香港中文大学统计系。研究方向包括函数型数据相关模型的统计推断问题，高位数据分析，分位数回归。