气候变化领域集成服务门户(CCPortal): Functional principal component analysis for incomplete space-time data

CCPortal

DOI	10.1007/s10651-024-00598-7
	Functional principal component analysis for incomplete space-time data
	Palummo, Alessandro; Arnone, Eleonora; Formaggia, Luca; Sangalli, Laura M.
发表日期	2024
ISSN	1352-8505
EISSN	1573-3009
起始页码	31
结束页码	2
卷号	31 期号:2
英文摘要	Environmental signals, acquired, e.g., by remote sensing, often present large gaps of missing observations in space and time. In this work, we present an innovative approach to identify the main variability patterns, in space-time data, when data may be affected by complex missing data structures. We formalize the problem in the framework of functional data analysis, proposing an innovative method of functional principal component analysis (fPCA) for incomplete space-time data. The functional nature of the proposed method permits to borrow information from measurements observed at nearby spatio-temporal locations. The resulting functional principal components are smooth fields over the considered spatio-temporal domain, and can lead to interesting insights in the spatio-temporal dynamic of the phenomenon under study. Moreover, they can be used to provide a reconstruction of the missing entries, also under severe missing data patterns. The proposed model combines a weighted rank-one approximation of the data matrix with a roughness penalty. We show that the estimation problem can be solved using a majorize-minimization approach, and provide a numerically efficient algorithm for its solution. Thanks to a discretization based on finite elements in space and B-splines in time, the proposed method can handle multidimensional spatial domains with complex shapes, such as water bodies with complicated shorelines, or curved spatial regions with complex orography. As shown by simulation studies, the proposed space-time fPCA is superior to alternative techniques for Principal Component Analysis with missing data. We further highlight the potentiality of the proposed method for environmental problems, by applying space-time fPCA to the study of the lake water surface temperature (LWST) of Lake Victoria, in Central Africa, starting from satellite measurements with large gaps. LWST is considered one of the fundamental indicators of how climate change is affecting the environment, and is recognized as an essential climate variable.
英文关键词	Functional data analysis; Majorization-minimization; Missing data; Roughness penalties
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Mathematics
WOS类目	Environmental Sciences ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:001184396600001
来源期刊	ENVIRONMENTAL AND ECOLOGICAL STATISTICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302607
作者单位	Polytechnic University of Milan; University of Turin
推荐引用方式 GB/T 7714	Palummo, Alessandro,Arnone, Eleonora,Formaggia, Luca,et al. Functional principal component analysis for incomplete space-time data[J],2024,31(2).
APA	Palummo, Alessandro,Arnone, Eleonora,Formaggia, Luca,&Sangalli, Laura M..(2024).Functional principal component analysis for incomplete space-time data.ENVIRONMENTAL AND ECOLOGICAL STATISTICS,31(2).
MLA	Palummo, Alessandro,et al."Functional principal component analysis for incomplete space-time data".ENVIRONMENTAL AND ECOLOGICAL STATISTICS 31.2(2024).