Prediction of functional data with spatial dependence: a penalized approach

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• 作者：M. Carmen Aguilera-Morillo ; María Durbán…
• 关键词：Spatial functional data ; Spatial correlation ; P ; spline penalty ; Functional regression
• 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：7-22
• 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 is focus on spatial functional variables whose observations are a set of spatially correlated sample curves obtained as realizations of a spatio-temporal stochastic process. In this context, as alternative to other geostatistical techniques (kriging, kernel smoothing, among others), a new method to predict the curves of temporal evolution of the process at unsampled locations and also the surfaces of geographical evolution of the variable at unobserved time points is proposed. In order to test the good performance of the proposed method, two simulation studies and an application with real climatological data have been carried out. Finally, the results were compared with ordinary functional kriging.