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Robust multivariate and functional archetypal analysis with application to financial time series analysis

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  • Moliner, Jesús
  • Epifanio, Irene

Abstract

Archetypal analysis approximates data by means of mixtures of actual extreme cases (archetypoids) or archetypes, which are a convex combination of cases in the data set. Archetypes lie on the boundary of the convex hull. This makes the analysis very sensitive to outliers. A robust methodology by means of M-estimators for classical multivariate and functional data is proposed. This unsupervised methodology allows complex data to be understood even by non-experts. The performance of the new procedure is assessed in a simulation study, where a comparison with a previous methodology for the multivariate case is also carried out, and our proposal obtains favorable results. Finally, robust bivariate functional archetypoid analysis is applied to a set of companies in the S&P 500 described by two time series of stock quotes. A new graphic representation is also proposed to visualize the results. The analysis shows how the information can be easily interpreted and how even non-experts can gain a qualitative understanding of the data.

Suggested Citation

  • Moliner, Jesús & Epifanio, Irene, 2019. "Robust multivariate and functional archetypal analysis with application to financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 195-208.
    • Handle: RePEc:eee:phsmap:v:519:y:2019:i:c:p:195-208
    DOI: 10.1016/j.physa.2018.12.036
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    References listed on IDEAS

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    3. Aleix Alcacer & Irene Epifanio & M Victoria Ibáñez & Amelia Simó & Alfredo Ballester, 2020. "A data-driven classification of 3D foot types by archetypal shapes based on landmarks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.

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