Principal component analysis with autocorrelated data. Issue 12 (12th August 2020)

• Record Type: Journal Article
• Authors: Zamprogno, Bartolomeu; Reisen, Valdério A.; Bondon, Pascal; Aranda Cotta, Higor H.; Reis, Neyval C.
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 12(2020)
• Page Start: 2117
• Page End: 2135
• Publication Date: 2020-08-12
• Subjects: Principal component analysis -- autocorrelation -- cross-correlation -- eigenvalue -- air pollution; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1764556
• Languages: English
• ISSNs: 0094-9655

Abstract

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.