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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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Submitted on : Tuesday, August 10, 2021 - 12:21:14 PM
Last modification on : Wednesday, August 11, 2021 - 3:29:26 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, 90 (12), pp.2117-2135. ⟨10.1080/00949655.2020.1764556⟩. ⟨hal-02560885⟩



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