Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia.
Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia.
Int J Environ Res Public Health. 2021 Jun 23;18(13):6754. doi: 10.3390/ijerph18136754.
This article proposes a novel data selection technique called the mixed peak-over-threshold-block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold . The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.
本文提出了一种新的数据选择技术，称为混合超越阈值块极大值（POT-BM）方法，用于对不健康的空气污染事件进行建模。POT 技术用于获取一组包含满足特定阈值以上的极端事件标准的数据点的块。这些选定的组被定义为 POT 块。与此同时，使用解聚类技术来克服在相邻 POT 块之间发生的依赖性行为问题。最后，集成 BM 概念以确定每个 POT 块的最大数据点。结果表明，混合 POT-BM 方法确定的极端数据点满足极端事件的独立特性，具有令人满意的拟合模型精度结果。总体而言，本研究得出结论，混合 POT-BM 方法在极端值事件的统计建模中提供了偏差和方差之间的平衡权衡。通过对马来西亚巴生的一个不健康的空气污染事件进行建模，进行了一个基于阈值 u > 100 的极端事件的案例研究。
