应用数学2026，Vol.39Issue(2)：397-413,17.
多效应GTWPR模型的局部线性极大似然估计
Local Linear Maximum Likelihood Estimation for Multi-effect Geographically and Temporally Weighted Poisson Regression Models
摘要
Abstract
Geographically and temporally weighted Poisson regression(GTWPR)models extend geographically and temporally weighted regression(GTWR)by accommodating count data while account-ing for spatial and temporal heterogeneity.Leveraging the advantages of local linear fitting techniques for mitigating boundary effects,this paper proposes a local linear maximum likelihood estimation(LLMLE)method for GTWPR.Furthermore,based on the idea of coefficient averaging,we present a two-stage local linear maximum likelihood estimation(TSLLMLE)method for multi-effect geographically and temporal-ly weighted Poisson regression(MEGTWPR)models.This approach can fit nonstationarity in certain explanatory variables across spatiotemporal,spatial,and temporal changes,while also characterizing glob-ally stationary for other variables,thereby improving the estimation accuracy of coefficients.Numerical simulations show that,compared with the existing coefficient-average-based estimation(CABE)method,the TSLLMLE method yields more accurate coefficient estimates and better boundary performance.关键词
时空地理加权泊松回归模型/多效应时空地理加权泊松回归模型/局部线性极大似然估计/两阶段估计Key words
Geographically and temporally weighted Poisson regression model/Multi-effect geo-graphically and temporally weighted Poisson regression model/Local linear maximum likelihood estima-tion/Two-stage estimation分类
数理科学引用本文复制引用
殷梦娜,张辉国..多效应GTWPR模型的局部线性极大似然估计[J].应用数学,2026,39(2):397-413,17.基金项目
新疆自然科学基金(2023D01C01) （2023D01C01）
教育部人文社会科学研究规划基金项目(19YJA910007) （19YJA910007）
