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
陈宇庆 1凌能祥1
作者信息
- 1. 合肥工业大学 数学学院,合肥 230601
- 折叠
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.
Abstract
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/PM2.5Key words
functional linear model/missing at random/composite quantile regression/PM2.5引用本文复制引用
出版年
2024
NETL
NSTL
万方数据