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- W4386098336 abstract "Air pollution seriously affects the production and life of human society, causing weather and climate to exhibit anomalies. The high variability of such data makes it difficult for traditional methods to accurately detect anomalous values. In order to solve such problems, this paper generalizes the 𝐿<sub>∞</sub> depth function according to its easy computational characteristics on high-dimensional data and applies the depth function to the detection of outliers in function data analysis. On simulated data, weighted 𝐿<sup>∞</sup> function depth is more robust in estimating the mean function compared to other function depths, with the smallest mean integrated squared error in estimating the mean function, and TPR is more efficient in identifying outliers. This approach is used to identify outliers in the data on air pollution in Shijiazhuang City, and it is compared with the typical boxplot method and other outlier detection techniques based on depth functions. The results show that the weight 𝐿<sub>∞</sub> method can identify the most polluted months of 𝑃𝑀<sub>10</sub>, 𝑃𝑀<sub>2.5</sub>, 𝑆𝑂<sub>2</sub> and 𝑁𝑂<sub>2</sub>, confirming the effectiveness of our proposed method. It provides a scientific basis for the improvement of weather regulation and control measures for heavy pollution in Shijiazhuang city." @default.
- W4386098336 created "2023-08-24" @default.
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- W4386098336 date "2023-08-23" @default.
- W4386098336 modified "2023-09-26" @default.
- W4386098336 title "Detection of outliers in air pollution from Shijiazhuang using functional data analysis" @default.
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- W4386098336 doi "https://doi.org/10.1117/12.2692369" @default.
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