报告简介：

In various applications with large spatial regions, the relationship between the response variable and the covariates is expected to exhibit complex spatial patterns. We propose a spatially clustered varying coefficient model, where the regression coefficients are allowed to vary smoothly within each cluster but change abruptly across the boundaries of adjacent clusters, and we develop a unified approach for simultaneous coefficient estimation and cluster identification. The varying coefficients are approximated by penalized splines, and the clusters are identified through a fused concave penalty on differences in neighboring locations, where the spatial neighbors are specified by the minimum spanning tree (MST). The optimization is solved efficiently based on the alternating direction method of multipliers, using the sparsity structure from MST. Furthermore, we establish the oracle property of the proposed method considering the structure of MST. Numerical studies show that the proposed method can efficiently incorporate spatial neighborhood information and automatically detect possible spatially clustered patterns in the regression coefficients. An empirical study in oceanography illustrates that the proposed method is promising to provide informative results.

报告人简介：

唐炎林，研究员，博士生导师，上海市浦江人才计划入选者。2012年1月博士毕业于复旦大学统计系，同年5月加入同济大学数学系，期间2015.9-2017.8在乔治华盛顿大学进行博士后研究，2019年1月加入华东师范大学统计学院。主要研究方向为分位数回归、高维数据统计推断、模型选择、复杂数据分析，主持国自科面上项目、青年项目、上海市自科面上项目各一项，在Biometrika、Journal of the Royal Statistical Society (Series B)、PNAS、Statistica Sinica、Biometrics、Scandinavian Journal of Statistics、Science China: Mathematics等SCI期刊发表论文30余篇，目前担任Journal of Korean Statistical Society的Associate Editor。