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

Authors :
Chunli Huang
Xu Zhao
Weihu Cheng
Qingqing Ji
Qiao Duan
Yufei Han
Source :
Mathematics, Vol 10, Iss 9, p 1433 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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 PM2.5 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 PM2.5 concentration, achieving ideal results.

Details

Language :
English
ISSN :
22277390 and 64710858
Volume :
10
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.4a62b65299e6471085882d92709806b8
Document Type :
article
Full Text :
https://doi.org/10.3390/math10091433