中国海洋大学学报（自然科学版）2024，Vol.54Issue(1)：156-164,9.DOI:10.16441/j.cnki.hdxb.20220201
空间多观测样本的地理加权回归模型
Geographically Weighted Regression for Spatial Data with Replicates
摘要
Abstract
The paper extends the classical GWR and constants a multiple replicates geographically weighted regression(MRGWR)that is able to handle an arbitrary number of replicates at each individual location.MRGWR adequately utilizes the similarity of regression relationships at neighboring locations when estimating regression coefficients,and imposes different weights on the replicates at neighboring locations.Moreover,it can directly handle the case of arbitrary number of replicates at each location.Numerical experiments are conducted to estimate the performance of the proposed model and to compare with ordinary least squares regression(OLSR)and GWR models.Furthermore,MRGWR is applied to resolving an important scientific problem in oceanography,i.e.,the ocean mesoscale eddy thermal feedback,revealing the seasonal and spatial variations in the response relationship between mesoscale sea surface heat flux anomalies and mesoscale sea surface temperature(SST)anomalies in the North Pacific.关键词
变系数回归/多观测样本/普通最小二乘回归/地理加权回归/中尺度涡旋热反馈Key words
varying coefficient regression/multiple replicates/ordinary least squares regression/geo-graphically weighted regression/mesoscale eddy thermal feedback分类
数理科学引用本文复制引用
栗春晓,李芙蓉..空间多观测样本的地理加权回归模型[J].中国海洋大学学报（自然科学版）,2024,54(1):156-164,9.基金项目
国家自然科学基金项目(41906011)资助 Supported by the National Nature Science Foundation of China(41906011) （41906011）
