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

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Visualizing Dependence of Bootstrap Confidence Intervals for Methods Yielding Spatial Configurationssuch analyses are applied to data for a sample drawn from a population. To assess how accurate the sample based plot is as a representation for the population, confidence intervals or ellipsoids can be constructed around each plotted point, using the bootstrap procedure. However, such a procedure ig

Ventilator

Automatic Discount Selection for Exponential Family State-Space Models on a sequential optimization of a Bayes factor and is intended for on-line modelling purposes. In this paper, these results are extended to state-space models where the distribution of the observable variable belongs to the exponential family.

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The Effects of MEP Distributed Random Effects on Variance Component Estimation in Multilevel Modelsifications on random effect distribution. The multivariate distributions here introduced for the random effects belong to the family of the Multivariate Exponential Power (MEP) distributions. Our primary interest is devoted to the variability of such estimators, since the MEPs have a noteworthy infl

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https://doi.org/10.1057/9781403983084ly mainly because of the several difficult issues involved. Among several available methods, genetic algorithms proved to be able to handle efficiently this topic. Several partitions are considered and iteratively selected according to some adequacy criterion. In this artificial “struggle for surviv

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https://doi.org/10.1007/978-3-030-30081-4005) 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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https://doi.org/10.1007/978-3-030-30081-4 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