Time：2020/12/8 14:00
Form：Tencent Meeting

Topic：Penalized local polynomial regression for spatial data

Abstract:

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.

Resume:

Wu Wang is an assistant professor at Renmin University of China. He graduated from Department of Statistics, Fudan University, and worked as a postdoctoral fellow at King Abdullah University of Science and Technology, Saudi Arabia. His research focuses on functional data analysis, spatial data analysis, and quantile regression. He is also interested in machine learning methods in energy and engineering applications.