基于两阶段自适应Lasso方法的乌鲁木齐市PM2.5浓度建模研究
The Modeling Study of PM2.5 Concentration in Urumqi City Based on the Two-Stage Adaptive Lasso Method
DOI: 10.12677/pm.2025.151036, PDF,    科研立项经费支持
作者: 阿依木古丽·图尔洪, 董翠玲*：新疆师范大学数学科学学院，新疆 乌鲁木齐
关键词: 两阶段多变点估计；自适应Lasso；线性回归模型；PM2.5；Two-Stage Multiple Change-Point Estimation； Adaptive Lasso； Linear Regression Model； PM2.5
摘要: 细颗粒物浓度是评估空气质量的重要指标之一，精确预测PM2.5浓度仍是国内外研究的挑战，也是政府和学术界关注的重点。文章以2014年1月1日至2024年9月30日的乌鲁木齐市PM2.5日平均浓度为研究对象，对其线性回归模型应用两阶段自适应Lasso多变点检测与估计方法检测并估计回归系数的多变点，建立了更精准的分段线性回归模型来刻画乌鲁木齐市PM2.5日平均浓度。
Abstract: The concentration of fine particulate matter (PM2.5) is one of the key indicators for assessing air quality. Accurate prediction of PM2.5 concentration remains a challenge in both domestic and international research and is a major focus of attention for governments and scholars. This study takes the daily average PM2.5 concentrations in Urumqi City from January 1, 2014 to September 30, 2024 as the research object. A two-stage adaptive Lasso multiple change-point detection and estimation method is applied to detect and estimate multiple change points in the regression coefficients of the linear regression City daily average PM2.5 concentrations in Urumqi City.
文章引用：阿依木古丽·图尔洪, 董翠玲. 基于两阶段自适应Lasso方法的乌鲁木齐市PM2.5浓度建模研究[J]. 理论数学, 2025, 15(1): 342-353. https://doi.org/10.12677/pm.2025.151036

参考文献

[1] 朱少钧, 董雯, 许嘉钰. 乌鲁木齐市区PM2.5污染特征及其溯源与追踪分析[J]. 新疆环境保护, 2012, 34(3): 6-11.
[2] 蒋雪峰, 李洁, 蒋奎, 等. 空气中PM2.5问题的建模研究[J]. 数学的实践与认识, 2014, 44(15): 47-58.
[3] 胡玉筱, 段显明. 基于高斯烟羽和多元线性回归模型的PM2.5扩散和预测研究[J]. 干旱区资源与环境, 2015, 29(6): 86-92.
[4] 刘杰, 杨鹏, 吕文生, 等. 基于气象因素的PM2.5质量浓度预测模型[J]. 山东大学学报(工学版), 2015, 45(6): 76-83.
[5] 盛永财, 玉米提∙哈力克, 阿不都拉∙阿不力孜. 气象因素对乌鲁木齐市PM2.5浓度影响分析[J]. 环境工程, 2018, 36(11): 64-69.
[6] 张纯曦, 阿丽亚∙拜都热拉, 刘丽, 等. 乌鲁木齐市PM2.5分布特征及其预测模型研究[J]. 石河子大学学报(自然科学版), 2020, 38(5): 648-654.
[7] 张一准, 颜七笙. 基于MRMR-SSA-BP的PM2.5浓度预测模型[J]. 计算机仿真, 2023, 40(8): 511-517.
[8] 张晗. 基于广义可加模型的PM2.5浓度组合预测方法[D]: [硕士学位论文]. 兰州: 西北师范大学, 2024.
[9] Davis, R.A., Lee, T.C.M. and Rodriguez-Yam, G.A. (2006) Structural Break Estimation for Nonstationary Time Series Models. Journal of the American Statistical Association, 101, 223-239. [Google Scholar] [CrossRef] 
[10] Jin, B., Shi, X. and Wu, Y. (2011) A Novel and Fast Methodology for Simultaneous Multiple Structural Break Estimation and Variable Selection for Nonstationary Time Series Models. Statistics and Computing, 23, 221-231. [Google Scholar] [CrossRef] 
[11] Jin, B., Wu, Y. and Shi, X. (2016) Consistent Two‐Stage Multiple Change‐Point Detection in Linear Models. Canadian Journal of Statistics, 44, 161-179. [Google Scholar] [CrossRef] 
[12] 周佳琪, 金百锁. 基于空间网络自回归变点模型的合肥市房地产价格影响因素分析[J]. 中国科学院大学学报, 2020, 37(3): 398-404.
[13] Sun, X. and Wu, Y. (2020) Simultaneous Multiple Change Points Estimation in Generalized Linear Models. In: Fan, J. and Pan, J., Eds., Contemporary Experimental Design, Multivariate Analysis and Data Mining, Springer International Publishing, 341-356. [Google Scholar] [CrossRef] 
[14] 吕丽, 金百锁. 线性模型中多变点的置信区间估计[J]. 系统科学与数学, 2021, 41(8): 2310-2326.
[15] 李美琪, 金百锁, 董翠玲. 线性回归模型中相依数据的多结构变点的估计[J]. 中国科学: 数学, 2023, 53(7): 1007-1024.
[16] Ding, H., Zhang, Y. and Wu, Y. (2021) A Novel Group VIF Regression for Group Variable Selection with Application to Multiple Change-Point Detection. Journal of Applied Statistics, 50, 247-263. [Google Scholar] [CrossRef] [PubMed]
[17] 廖红怡, 金百锁. 多阈值负二项回归模型[J]. 应用概率统计, 2024, 40(4): 684-696.
[18] Tibshirani, R. (1996) Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58, 267-288. [Google Scholar] [CrossRef] 
[19] Zou, H. (2006) The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, 101, 1418-1429. [Google Scholar] [CrossRef] 
[20] Horváth, L. and Csörgö, M. (1997) Limit Theorems in Change-Point Analysis. Wiley, 218-219.

为你推荐