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Journal articles
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.
https://hal-centralesupelec.archives-ouvertes.fr/hal-02560885
Contributor : Pascal Bondon Connect in order to contact the contributor
Submitted on : Tuesday, August 10, 2021 - 12:21:14 PM
Last modification on : Tuesday, January 4, 2022 - 6:43:14 AM
Long-term archiving on: : Thursday, November 11, 2021 - 6:09:05 PM
Contributor : Pascal Bondon Connect in order to contact the contributor
Submitted on : Tuesday, August 10, 2021 - 12:21:14 PM
Last modification on : Tuesday, January 4, 2022 - 6:43:14 AM
Long-term archiving on: : Thursday, November 11, 2021 - 6:09:05 PM
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