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Analysis of Large and Complex Data pp 173–183Cite as

Multivariate Functional Regression Analysis with Application to Classification Problems

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Abstract

Multivariate functional data analysis is an effective approach to dealing with multivariate and complex data. These data are treated as realizations of multivariate random processes; the objects are represented by functions. In this paper we discuss different types of regression model: linear and logistic. Various methods of representing functional data are also examined. The approaches discussed are illustrated with an application to two real data sets.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614, Poznań, Poland

    Tomasz Górecki, Mirosław Krzyśko & Waldemar Wołyński

Authors
  1. Tomasz Górecki
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  2. Mirosław Krzyśko
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  3. Waldemar Wołyński
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Corresponding author

Correspondence to Tomasz Górecki .

Editor information

Editors and Affiliations

  1. Jacobs University Bremen , Bremen, Germany

    Adalbert F.X. Wilhelm

  2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany

    Hans A. Kestler

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© 2016 Springer International Publishing Switzerland

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Górecki, T., Krzyśko, M., Wołyński, W. (2016). Multivariate Functional Regression Analysis with Application to Classification Problems. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_15

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