%0 Journal Article
%T 基于两阶段自适应Lasso方法的乌鲁木齐市PM<sub>2.5</sub>浓度建模研究<br>The Modeling Study of PM<sub>2.5</sub> Concentration in Urumqi City Based on the Two-Stage Adaptive Lasso Method
%A 阿依木古丽·图尔洪
%A 董翠玲
%J Pure Mathematics
%P 342-353
%@ 2160-7605
%D 2025
%I Hans Publishing
%R 10.12677/pm.2025.151036
%X 细颗粒物浓度是评估空气质量的重要指标之一&#65292;精确预测PM<sub>2.5</sub>浓度仍是国内外研究的挑战&#65292;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PM<sub>2.5</sub>日平均浓度为研究对象&#65292;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&#65292;建立了更精准的分段线性回归模型来刻画乌鲁木齐市PM<sub>2.5</sub>日平均浓度。<br>The concentration of fine particulate matter (PM<sub>2.5</sub>) is one of the key indicators for assessing air quality. Accurate prediction of PM<sub>2.5</sub> 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 PM<sub>2.5</sub> 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 PM<sub>2.5</sub> concentrations in Urumqi City.
%K 两阶段多变点估计&#65292
%K 自适应Lasso&#65292
%K 线性回归模型&#65292
%K PM<sub>2.</sub><sub>5</sub><br>Two-Stage Multiple Change-Point Estimation
%K Adaptive Lasso
%K Linear Regression Model
%K PM<sub>2.5</sub>
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=106683