Special Issue 2022 – Call for Papers
A New Role for Statistics: The Joint Special Issue of "Statistics in Transition New Series" (SiTns) and "Statystyka Ukraïny" (SU)
Tomasz Górecki https://orcid.org/0000-0002-9969-5257 , Mirosław Krzyśko https://orcid.org/0000-0001-0075-4432 , Waldemar Wołyński https://orcid.org/0000-0002-0777-9163

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A new variable selection method is considered in the setting of classification with multivariate functional data (Ramsay and Silverman (2005)). The variable selection is a dimensionality reduction method which leads to replace the whole vector process, with a low-dimensional vector still giving a comparable classification error. Various classifiers appropriate for functional data are used. The proposed variable selection method is based on functional distance covariance (dCov) given by Székely and Rizzo (2009, 2012) and the Hilbert-Schmidt Independent Criterion (HSIC) given by Gretton et al. (2005). This method is a modification of the procedure given by Kong et al. (2015). The proposed methodology is illustrated with a real data example.


multivariate functional data, variable selection, dCov, HSIC, classification


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