Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors

doi:10.3390/math10091433

	Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors
	Huang, Chunli 1; Zhao, Xu 1; Cheng, Weihu 1; Ji, Qingqing 2,3; Duan, Qiao ; Han, Yufei 5
	2022-05-01
发表期刊	MATHEMATICS
卷号	10 期号:9 页码:25
摘要	Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM 2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM 2.5 concentration, achieving ideal results.
关键词	generalized Pareto distribution peaks over threshold dynamic conditional autoregressive modeling threshold selection long short-term memory
DOI	10.3390/math10091433
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11801019]
WOS研究方向	Mathematics
WOS类目	Mathematics
WOS记录号	WOS:000795220300001
出版者	MDPI
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19547
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Xu
作者单位	1.Beijing Univ Technol, Fac Sci, Beijing 100124, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Beijing Univ Technol, Fac Humanities & Social Sci, Beijing 100124, Peoples R China 5.Beijing Univ Posts & Telecommunicat, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Huang, Chunli,Zhao, Xu,Cheng, Weihu,et al. Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors[J]. MATHEMATICS,2022,10(9):25.
APA	Huang, Chunli,Zhao, Xu,Cheng, Weihu,Ji, Qingqing,Duan, Qiao,&Han, Yufei.(2022).Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors.MATHEMATICS,10(9),25.
MLA	Huang, Chunli,et al."Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors".MATHEMATICS 10.9(2022):25.