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Principal component analysis with autocorrelated data
Journal of Statistical Computation and Simulation ( IF 1.1 ) Pub Date : 2020-05-15 , DOI: 10.1080/00949655.2020.1764556
Bartolomeu Zamprogno 1, 2 , Valdério A. Reisen 1, 2, 3 , Pascal Bondon 3 , Higor H. Aranda Cotta 1, 2, 3 , Neyval C. Reis 1
Affiliation  

This paper contributes to the analysis, interpretation and the use of the principal component analysis in a multivariate time-correlated linear process. The effect of ignoring the autocorrelation structure of the vector process is investigated. The results show a spurious impact of the time-correlation on the eigenvalues. To mitigate this impact, a pre-filtering procedure to whiten the data is applied. The methodology is used to identify redundant particulate matter measurements in a region in Brazil. Among the eight considered monitoring stations, it is found that three are needed to characterize the dynamic of the pollutant in the region.

中文翻译:

使用自相关数据进行主成分分析

本文有助于在多元时间相关线性过程中分析、解释和使用主成分分析。研究了忽略向量过程的自相关结构的影响。结果显示了时间相关性对特征值的虚假影响。为了减轻这种影响,应用了预过滤程序来白化数据。该方法用于识别巴西某个地区的冗余颗粒物测量值。在考虑的八个监测站中,发现需要三个来表征该地区污染物的动态。
更新日期:2020-05-15
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