Multivariate functional random fields: prediction and optimal sampling

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• 作者：M. Bohorquez ; R. Giraldo ; J. Mateu
• 关键词：Functional data ; Multivariate geostatistics ; Optimal sampling
• 刊名：Stochastic Environmental Research and Risk Assessment
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：31
• 期：1
• 页码：53-70
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Math. Appl. in Environmental Science; Earth Sciences, general; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Computa
• 出版者：Springer Berlin Heidelberg
• ISSN：1436-3259
• 卷排序：31

文摘

This paper develops spatial prediction of a functional variable at unsampled sites, using functional covariates, that is, we present a functional cokriging method. We show that through the representation of each function in terms of its empirical functional principal components, the functional cokriging only depends on the auto-covariance and cross-covariance of the associated scores vectors, which are scalar random fields. In addition, we propose the methodology to find optimal sampling designs in this context. The proposal is applied to the network of air quality in México city.