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Principal component analysis of multivariate spatial functional data
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DOI：10.1016/j.bdr.2024.100504.png)
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摘要
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This paper is devoted to the study of dimension reduction techniques for multivariate spatially indexed functional data and defined on different domains. We present a method called Spatial Multivariate Functional Principal Component Analysis (SMFPCA), which performs principal component analysis for multivariate spatial functional data. In contrast to Multivariate Karhunen-Lo & egrave;ve approach for independent data, SMFPCA is notably adept at effectively capturing spatial dependencies among multiple functions. SMFPCA applies spectral functional component analysis to multivariate functional spatial data, focusing on data points arranged on a regular grid. The methodological framework and algorithm of SMFPCA have been developed to tackle the challenges arising from the lack of appropriate methods for managing this type of data. The performance of the proposed method has been verified through finite sample properties using simulated datasets and sea-surface temperature dataset. Additionally, we conducted comparative studies of SMFPCA against some existing methods providing valuable insights into the properties of multivariate spatial functional data within a finite sample.
Keyword:
Spectral analysis
Functional data analysis
Functional principal component analysis
Spatial-functional principal component
analysis
Multivariate analysis
期刊
IF:
4.2
论文数:
417
被引数:
1.1K
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Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains在不同 (维) 域上观察到的数据的多元功能主成分分析
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