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Principal component analysis with autocorrelated data

Abstract : 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.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02560885
Contributor : Pascal Bondon <>
Submitted on : Tuesday, March 30, 2021 - 4:53:55 PM
Last modification on : Thursday, April 1, 2021 - 3:35:36 AM

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Bartolomeu Zamprogno, Valderio A. Reisen, Pascal Bondon, Higor Henrique Aranda Cotta, Neyval Costa Reis Júnior. Principal component analysis with autocorrelated data. Journal of Statistical Computation and Simulation, Taylor & Francis, 2020, ⟨10.1080/00949655.2020.1764556⟩. ⟨hal-02560885⟩

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