讲座信息

讲座信息

您当前的位置: 讲座信息
20201208王武:Penalized local polynomial regression for spatial data
时间:2020-12-01

报告时间:2020年12月8日下午14:00

报告形式:腾讯会议

报告嘉宾:王武

报告主题:Penalized local polynomial regression for spatial data


报告摘要

When performing spatial regression analysis in environmental data applica- tions, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regres- sion and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations. The local polynomial parameters have attractive interpretations, indicating various types of spatial heterogeneity. Instead of estimating the spatially varying regression coefficients directly, we develop a penalized least squares regression procedure for the local polynomial parameter estimation, which both shrinks the parameter estimation and penalizes the differences among parameters that are associated with neighboring locations. We develop confidence intervals for the varying regression coefficients and prediction intervals for the response. We apply the proposed method to characterize the spatially varying association between particulate matter concentrations (PM2.5) and pollutant gases related to the secondary aerosol formulation in China. The identified regression coefficients show distinct spatial patterns for nitrogen dioxide, sulfur dioxide, and carbon monoxide during different seasons.



个人简介

王武,中国人民大学数理统计系讲师,沙特阿拉伯阿卜杜拉国王科技大学博士后,复旦大学数理统计博士。主要研究方向是函数型数据分析、空间数据分析、机器学习和深度学习方法在能源、工业领域的应用等。成果发表于BiometricsScandinavian Journal of Statistics等期刊。


主持人简介

孙怡帆,中国人民大学统计学院副教授,博士生导师。数理统计系系主任,全国工业统计学教学研究会第九届理事会理事。主要研究方向为复杂数据分析、网络分析、最优化方法等,在Statistics in Medicine,统计研究等学术期刊发表论文20余篇,主持国家和省部级等项目6项。




扫描下方二维码报名

所有消息会在两个群中同步通知

请大家不要重复加群~