Selected statistical methods of data analysis for multivariate functional data
Tomasz Górecki (tomasz.gorecki@amu.edu.pl),
Mirosław Krzyśko,
Łukasz Waszak and
Waldemar Wołyński
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Tomasz Górecki: Adam Mickiewicz University
Mirosław Krzyśko: Adam Mickiewicz University
Łukasz Waszak: Adam Mickiewicz University
Waldemar Wołyński: Adam Mickiewicz University
Statistical Papers, 2018, vol. 59, issue 1, No 7, 153-182
Abstract:
Abstract 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. The paper is devoted to three statistical dimension reduction techniques for multivariate data. For the first one, principal components analysis, the authors present a review of a recent paper (Jacques and Preda in, Comput Stat Data Anal, 71:92–106, 2014). For two others one, canonical variables and discriminant coordinates, the authors extend existing works for univariate functional data to multivariate. These methods for multivariate functional data are presented, illustrated and discussed in the context of analyzing real data sets. Each of these techniques is applied on real data set.
Keywords: Multivariate functional data; Functional data analysis; Principal component analysis; Discriminant coordinates; Canonical correlation analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (15)
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DOI: 10.1007/s00362-016-0757-8
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