基于函数型数据模型的PM2.5浓度与温度关系的实证分析  

Empirical Analysis of the Relationship between PM2.5 Concentration and Temperature Based on Functional Data Models

作　　者：陈宇庆 凌能祥[1] CHEN Yuqing;LING Nengxiang(School of Mathematics,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]合肥工业大学数学学院,合肥230601

出　　处：《大学数学》2024年第4期1-6,共6页College Mathematics

基　　金：国家自然科学基金(72071068)。

摘　　要：考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.Consider the relationship between a scalar response variable and partially observed functional covariates using a functional linear model.By employing Functional Principal Component Analysis(FPCA),we reconstruct the missing parts of the sample data.An empirical analysis is performed on a dataset of meteorological data,including partially observed PM2.5 values,collected in Beijing from 2010 to 2014.This analysis examines the impact of PM2.5,as a partially observed functional covariate,on the scalar response variable of average temperature.The results indicate that this method is practically significant for handling missing functional data.

关 键 词：函数线性模型 随机缺失 复合分位数回归 PM2.5 

分 类 号：O212.7[理学—概率论与数理统计]