By Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez
The subject of the assembly used to be “Statistical tools for the research of huge Data-Sets”. in recent times there was expanding curiosity during this topic; in reality a tremendous volume of knowledge is frequently to be had yet commonplace statistical recommendations are not well matched to handling this type of facts. The convention serves as a big assembly element for eu researchers engaged on this subject and a few eu statistical societies participated within the association of the development. The e-book contains forty five papers from a variety of the 156 papers authorised for presentation and mentioned on the convention on “Advanced Statistical tools for the research of huge Data-sets.”
Read or Download Advanced Statistical Methods for the Analysis of Large Data-Sets (Studies in Theoretical and Applied Statistics / Selected Papers of the Statistical Societies) PDF
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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)
In this application the k-mean alignment algorithm outdoes both functional k-mean clustering and Procrustes continuous alignment by pointing out interesting features from a medical and fluid dynamical point of view that former methods were not able to point out. , and Steinman, D. (2008), “An image-based modeling framework for patient-specific computational hemodynamics,” Medical and Biological Engineering and Computing, 1097–112. , and Meste, O. (2010), “Core Shape modelling of a set of curves,” Computational Statistics and Data Analysis, 308–325.
2010a,b) – originates from the need of consistently aligning and clustering a set of functional data. , Tarpey and Kinateder, 2003). With these two mother algorithms, the new algorithm shares both aims and basic operations. Schematic flowcharts of both Procrustes continuous registration algorithm and functional k-mean clustering algorithm are sketched in Fig. 1. Alternative approaches to the joint clustering and alignment of curves can be found for instance in Liu and Yang (2009), and Boudaoud et al.
Mateu. Statistics for Spatial Functional data. Environmetrics. Forthcoming. net/2117/2446, Universitat Politecnica de Catalunya, 2009. G. James, C. Sugar. Clustering for Sparsely Sampled Functional Data. Journal of the American Statistical Association, 98, 397–408, 2005. N. Heckman , R. Zamar Comparing the shapes of regression functions. Biometrika, 87, 135–144, 2000. , Saporta. PLS approach for clusterwise linear regression on functional data. In Classification, Clustering, and Data Mining Applications (D.