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Default Priors Based on Pseudo-Likelihoods for the Poisson-GPD Model Read chapter Read first chapter

2013 | OriginalPaper | Chapter

10. A Regionalization Method for Spatial Functional Data Based on Variogram Models: An Application on Environmental Data

Authors : Elvira Romano, Antonio Balzanella, Rosanna Verde

Published in: Advances in Theoretical and Applied Statistics

Publisher: Springer Berlin Heidelberg

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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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previous chapter Vector Threshold Moving Average Models: Model Specification and Invertibility
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Wise, S.M., Haining, R.P., MA, J.: Regionalization tools for the exploratory spatial analysis of health data. In: Fischer, M., Getis, A. (eds.) Recent Developments in Spatial Analysis: Spatial Statistics, Behavioural Modelling and Neuro-Computing, pp. 83-100. Springer, Berlin (1997)
Metadata
Title
A Regionalization Method for Spatial Functional Data Based on Variogram Models: An Application on Environmental Data
Authors
Elvira Romano
Antonio Balzanella
Rosanna Verde
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Advances in Theoretical and Applied Statistics

Print ISBN: 978-3-642-35587-5

Electronic ISBN: 978-3-642-35588-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-35588-2_10

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