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Methods of Reducing Dimension for Functional Data

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Listed:
  • Mirosław Krzyśko
  • Waldemar Wołyński
  • Tomasz Górecki
  • Łukasz Waszak

Abstract

In classical data analysis, objects are characterized by many features observed at one point of time. We would like to present them graphically, to see their configuration, eliminate outlying observations, observe relationships between them or to classify them. In recent years methods for representing data by functions have received much attention. In this paper we discuss a new method of constructing principal components for multivariate functional data. We illustrate our method with data from environmental studies.

Suggested Citation

  • Mirosław Krzyśko & Waldemar Wołyński & Tomasz Górecki & Łukasz Waszak, 2014. "Methods of Reducing Dimension for Functional Data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(2), pages 231-242, March.
    • Handle: RePEc:csb:stintr:v:15:y:2014:i:2:p:231-242
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    References listed on IDEAS

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    1. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
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    1. Mirosław Krzyśko & Peter Nijkamp & Waldemar Ratajczak & Waldemar Wołyński, 2022. "Multidimensional economic indicators and multivariate functional principal component analysis (MFPCA) in a comparative study of countries’ competitiveness," Journal of Geographical Systems, Springer, vol. 24(1), pages 49-65, January.

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