Abstract
In this paper we propose an extended version of a model-based strategy for clustering spatial functional data. The strategy, we refer, aims simultaneously to classify spatially dependent curves and to obtain a spatial functional model prototype for each cluster. The fit of these models implies to estimate a variogram function, the trace variogram function. Our proposal is to introduce an alternative estimator for the trace-variogram function: a kernel variogram estimator. This works better to adapt spatial varying features of the functional data pattern. Experimental comparisons show this approach has some advantages over the previous one.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abraham, C., Corillon, P., Matnzer-L\ddot{o}ber, E., & Molinari, N., (2005). Unsupervised curve clustering using B-splines. Scandinavian Journal of Statistics, 30, 581–595.
Cressie, N.A.C. (1993). Statistics for spatial data. New York: Wiley.
Diday, E. (1971). La Méthode des nuées dynamiques. Review the Statistics Applications, XXX(2), 19–34.
Delicado, P., Giraldo, R., & Mateu, J., (2007). Geostatistics for functional data: An ordinary kriging approach. Technical Report.http://hdl.handle.net/2117/1099, Universitat Politecnica de Catalunya.
Heckman, N., & Zamar, R. (2000). Comparing the shapes of regression functions. Biometrika, 87, 135–144.
James, G., & Sugar, C. (2005). Clustering for Sparsely Sampled Functional Data. Journal of the American Statistical Association, 98, 397–408.
Kakkar, S., (2004). Methodology for clustering spatio-temporal databases. Master Thesis, University of Cincinnati.
Keming, Y., Mateu, J., & Porcu, E. (2007). A kernel-based method for nonparametric estimation of variograms. Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, 61(2), 173–197.
Ramsay, J.E., & Silverman, B. W. (2005). Functional data analysis (2nd ed.). New York: Springer.
Romano, E. (2006).Dynamical curves clustering with free knots spline estimation. PhD Thesis. Naples: University of Federico II.
Romano, E., Balzanella, A., & Verde, R. (2009). Clustering spatio-functional data: A model based approach. In Proceedings of the 11th IFCS biennial conference and 33rd annual conference of the Gesellschaft für Klassifikation e.V., Dresden, March 13–18, 2009 Studies in classification, data analysis, and knowledge organization. Berlin-Heidelberg, New York: Springer. ISBN: 978-3-642-10744-3.
Stein, M.L. (1999).Interpolation of spatial data: Some theory for kriging. New York: Springer-Verlag.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Romano, E., Verde, R., Cozza, V. (2011). Clustering Spatial Functional Data: A Method Based on a Nonparametric Variogram Estimation. In: Ingrassia, S., Rocci, R., Vichi, M. (eds) New Perspectives in Statistical Modeling and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11363-5_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-11363-5_38
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11362-8
Online ISBN: 978-3-642-11363-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)