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  • Statistics In Transition

 

Article | 22-July-2019

VARIABLE SELECTION IN MULTIVARIATE FUNCTIONAL DATA CLASSIFICATION

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 Sz

Tomasz Górecki, Mirosław Krzyśko, Waldemar Wołyński

Statistics in Transition New Series, Volume 20 , ISSUE 2, 123–138

Research paper | 31-October-2017

CLASSIFICATION PROBLEMS BASED ON REGRESSION MODELS FOR MULTI-DIMENSIONAL FUNCTIONAL DATA

Data in the form of a continuous vector function on a given interval are referred to as multivariate functional data. These data are treated as realizations of multivariate random processes. We use multivariate functional regression techniques for the classification of multivariate functional data. The approaches discussed are illustrated with an application to two real data sets.

Tomasz Górecki, Mirosław Krzyśko, Waldemar Wołyński

Statistics in Transition New Series, Volume 16 , ISSUE 1, 97–110

Article | 06-July-2017

AN EXTENSION OF THE CLASSICAL DISTANCE CORRELATION COEFFICIENT FOR MULTIVARIATE FUNCTIONAL DATA WITH APPLICATIONS

Tomasz Górecki, Mirosław Krzy´sko, Waldemar Ratajczak, Waldemar Woły´nski

Statistics in Transition New Series, Volume 17 , ISSUE 3, 449–466

Article | 20-September-2020

Measuring and Testing Mutual Dependence of Multivariate Functional Data

Mirosław Krzyśko, Łukasz Smaga

Statistics in Transition New Series, Volume 21 , ISSUE 3, 21–37

Research Article | 20-November-2017

AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed

Mirosław Krzyśko, Łukasz Smaga

Statistics in Transition New Series, Volume 18 , ISSUE 3, 433–442

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