一、 青年教师工作论文报告

二、特邀专家报告

题目：Supervised spatial metric learning with applications to spatial clustering and spatial model prediction

摘 要：Spatial patterns and relationships are crucial for statistical modeling and inference across various fields. This study develops a novel approach using supervised Random Forest to compute similarity scores between locations, effectively capturing spatial dependencies of a response variable. The approach begins by enriching location coordinates, enabling Random Forest to split space into irregular shaped subspaces. The similarity score is then derived from the proportion of trees in which two locations fall in the same node for the same values of other predictors. From the resulting similarity matrix, eigen-scores and cluster labels are extracted and integrated into predictive models such as GWR, XGBoost, Random Forest, GAM, spatially and non-spatially varying coefficient (S&NVC) models and Spatial Durbin Model (SDM). Simulations and two real data analyses indicate that the similarity matrix can both capture more spatial information leading to meaningful clustering results and significantly enhances the predictive performance of various models.

报告人：Gu Hong

专家简介：Gu Hong，加拿大达尔豪斯大学统计系教授、博士生导师。1999年获香港大学博士学位。主要研究领域为计算生物学与生物信息学，尤其专注于统计建模、推断方法的开发及其在分子进化与宏基因组学中的应用与拓展。同时，Gu Hong教授对数据挖掘方法及其应用亦有浓厚兴趣，特别是在医学数据分析方面开展了深入研究。其相关成果已发表于Journal of the American Statistical Association、Statistics in Medicine 等国际权威期刊，并主持多项加拿大自然科学与工程研究理事会（NSERC）资助课题。