Generalized principal component analysis for moderately non-stationary vector time series
Alshammri, Fayed and Pan, Jiazhu (2021) Generalized principal component analysis for moderately non-stationary vector time series. Journal of Statistical Planning and Inference, 212. pp. 201-225. ISSN 0378-3758 (https://doi.org/10.1016/j.jspi.2020.08.007)
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Abstract
This paper extends the principal component analysis (PCA) to moderately non-stationary vector time series. We propose a method that searches for a linear transformation of the original series such that the transformed series is segmented into uncorrelated subseries with lower dimensions. A columns' rearrangement method is proposed to regroup transformed series based on their relationships. We discuss the theoretical properties of the proposed method for fixed and large dimensional cases. Many simulation studies show our approach is suitable for moderately non-stationary data. Illustrations on real data are provided.
ORCID iDs
Alshammri, Fayed and Pan, Jiazhu ORCID: https://orcid.org/0000-0001-7346-2052;-
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Item type: Article ID code: 73541 Dates: DateEventMay 2021Published12 November 2020Published Online2 August 2020AcceptedSubjects: Science > Mathematics > Probabilities. Mathematical statistics Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 11 Aug 2020 13:13 Last modified: 02 Oct 2024 00:35 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/73541