報告主題： Spatially Clustered Varying Coefficient Model

報告時間：11月4日（周五）10:00

報告地點：騰訊會議🛎，會議ID：986-742-014

報告時間🧑🏽‍⚖️👱🏻‍♀️：2022/11/04 10:00-11:00

報告人：唐炎林 華東師範大學 研究員

報告簡介🧑🏼‍🦲：

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.

報告人簡介：

唐炎林，研究員😨🙎🏼，博士生導師👨🏽‍🍳🩷，上海市浦江人才計劃入選者。2012年1月博士畢業於復旦大學統計系🧏🏿‍♀️，同年5月加入同濟大學數學系🤦🏻‍♂️，期間2015.9-2017.8在喬治華盛頓大學進行博士後研究，2019年1月加入華東師範大學統計沐鸣2🔧。主要研究方向為分位數回歸📟、高維數據統計推斷🙅🏽‍♂️、模型選擇、復雜數據分析🕒🪦，主持國自科面上項目🥭、青年項目👨🏻‍🦼、上海市自科面上項目各一項，在Biometrika👩🏿‍🏭、Journal of the Royal Statistical Society (Series B)、PNAS、Statistica Sinica、Biometrics、Scandinavian Journal of Statistics、Science China: Mathematics等SCI期刊發表論文30余篇，目前擔任Journal of Korean Statistical Society的Associate Editor。