北京PM2.5浓度预测模型的构建——基于部分线性回归分析  

CONSTRUCTION OF PM2.5 CONCENTRATION PREDICTION MODELINBEIJING——BASEDONPARTIALLINEAR REGRESSION ANALYSIS

作　　者：刘超 Liu Chao(Tianjin University of Finance and Economics Pearl Real College,Department of Statistics,Tianjin 300322,China)

机构地区：[1]天津财经大学珠江学院统计系,天津300322

出　　处：《数值计算与计算机应用》2024年第3期262-272,共11页Journal on Numerical Methods and Computer Applications

摘　　要：本文结合非参数回归模型和分位数回归模型,提出了基于Fourier基的部分线性回归模型.论文采用Fourier函数逼近模型的非线性函数部分,并结合分位数回归模型给出了该模型的估计方法.在一些基本的假设条件下给出了参数向量和非线性函数估计的一致性证明,并通过模拟研究显示该方法的有效性,在论文的最后利用该模型模型对北京首都国际机场的气象测量数据进行实证分析,为准确预测PM2.5的扩散情况提出新的方法.This paper proposes a partial linear regression model based on Fourier basis by com-bining nonparametric regression model and quantile regression model.We use the Fourier approximation of function model of the nonlinear function,and combined with quantile re-gression model estimation method of the model are given,under some basic assumptions The consistency of parameter vector and nonlinear function estimation is proved.The effec-tiveness of this method is demonstrated by simulation studies.At the end of the paper,the meteorological data of Beijing Capital International Airport are empirically analyzed with this model,and a new method will be proposed to accurately predict the diffusion of PM2.5 by this model.

关 键 词：Fourier基 纵向数据 分位数回归模型 非参数模型 

分 类 号：O212.1[理学—概率论与数理统计] X513[理学—数学]