Abstract
This chapter proposes a Dynamic Clustering Algorithm (DCA) as a new regionalization method for spatial functional data. The method looks for the best partition optimizing a criterion of spatial association among functional data. Furthermore it is such that a summary of the variability structure of each cluster is discovered. The performance of the proposal is checked through an application on real data.
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Romano, E., Balzanella, A., Verde, R. (2013). A Regionalization Method for Spatial Functional Data Based on Variogram Models: An Application on Environmental Data. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_10
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DOI: https://doi.org/10.1007/978-3-642-35588-2_10
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