By Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez
The subject matter of the assembly used to be “Statistical tools for the research of enormous Data-Sets”. in recent times there was expanding curiosity during this topic; in truth an incredible volume of data is usually to be had yet average statistical concepts usually are not well matched to dealing with this sort of info. The convention serves as a major assembly aspect for eu researchers engaged on this subject and a couple of ecu statistical societies participated within the association of the development. The publication comprises forty five papers from a variety of the 156 papers permitted for presentation and mentioned on the convention on “Advanced Statistical tools for the research of huge Data-sets.”
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Additional resources for Advanced Statistical Methods for the Analysis of Large Data-Sets (Studies in Theoretical and Applied Statistics / Selected Papers of the Statistical Societies)
It is based on a Dynamic Clustering Algorithm with on an optimization problem that minimizes the spatial variability among the curves in each cluster. A second one looks simultaneously for an optimal partition of spatial functional data set and a set of bivariate functional regression models associated to each cluster. These models take into account both the interactions among different functional variables and the spatial relations among the observations. 1 Introduction There is a large number of applicative fields like metereology, geology, agronomy, ecology, where data are curves observed in spatial varying way.
2009b) is a clusterwise linear regression approach that attempts to discover spatial functional linear regression models with two functional predictors, an interaction term, and with spatially correlated residuals. This approach is such to establish a spatial organization in relation to the interaction among different functional data. The algorithm is a k-means clustering with a criterion based on the minimization of the squared residuals instead of the classical within cluster dispersion. Both the strategies have the main aim of obtaining clusters in relation to the spatial interaction among functional data.
IEEE Computer Society, Washington, DC, 529-535, 2007. H. Cardot, F. Ferraty, P. Sarda. Functional linear model. Statistics and Probability Letters, 45:11– 22, 1999. E. Diday. La Methode K des nuees K dynamiques. Rev. Appl. XXX, 2, 19–34, 1971. P. Delicado, R. Giraldo, J. Mateu. Geostatistics for functional data: An ordinary kriging approach. net/2117/1099, Universitat Politecnica de Catalunya, 2007. P. Delicado, R. Giraldo, C. Comas, J. Mateu. Statistics for Spatial Functional data. Environmetrics.