Titlebook: Data Analysis, Classification and the Forward Search; Proceedings of the M Sergio Zani,Andrea Cerioli,Maurizio Vichi Conference proceedings

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Graphical Representation of Functional Clusters and MDS Configurations005) proposed functional data analysis. Functional data analysis enlarges the range of statistical data analysis. But, it is not easy to represent results of functional data analysis techniques. We focus on two methods of functional data analysis: functional clustering and functional MDS. We show gr

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Estimation of the Structural Mean of a Sample of Curves by Dynamic Time Warpingse, for computing the dissimilarity between curves, in this paper we modify the classical DTW in order to obtain discrete warping functions and to estimate the structural mean of a sample of curves. With the suggested methodology we analyze series of daily measurements of some air pollutants in Emil

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Sequential Decisional Discriminant Analysisg is based on the research of principal axes of a configuration of points in the individual-space with a relational inner product. We are in presence of a discriminant analysis problem, in which the decision must be taken as the partial knowledge evolutionary information of the observations of the s

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Regularized Sliced Inverse Regression with Applications in Classification extensively studied under the general subject of discriminant analysis in the statistical literature, or supervised pattern recognition in the machine learning field. Recently, dimension reduction methods, such as SIR. and SAVE, have been used for classification purposes. In this paper we propose a

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Missing Data in Optimal Scalingng data. The proposal is based on Nonlinear PCA technique to be jointly used with an . imputation method for the treatment of missing data. The procedure is particularly suitable when dealing with ordinal, or mixed, variables, which are strongly interrelated and in the presence of Specific patterns

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