Selected statistical methods of data analysis for multivariate 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. The paper is devoted to three statistical dimension reduction techniques for multivariate data. For the first...

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Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2018-03, Vol.59 (1), p.153-182
Main Authors: Górecki, Tomasz, Krzyśko, Mirosław, Waszak, Łukasz, Wołyński, Waldemar
Format: Article
Language:English
Subjects:
Continuity (mathematics)
Correlation analysis
Data analysis
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Insurance
Management
Mathematics and Statistics
Multivariate analysis
Operations Research/Decision Theory
Principal components analysis
Probability Theory and Stochastic Processes
Random processes
Regression analysis
Regular Article
Statistical methods
Statistics
Statistics for Business
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Summary: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.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-016-0757-8