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Classification Problems Based on Regression Models for Multi-Dimensional Functional Data

Author

Listed:
  • Górecki Tomasz

    (Adam Mickiewicz University, Mickiewicz, ; Poland)

  • Krzyśko Mirosław

    (Adam Mickiewicz University, Mickiewicz, ; Poland)

  • Wołyński Waldemar

    (Adam Mickiewicz University, Mickiewicz, ; Poland)

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. We use multivariate functional regression techniques for the classification of multivariate functional data. The approaches discussed are illustrated with an application to two real data sets.

Suggested Citation

  • Górecki Tomasz & Krzyśko Mirosław & Wołyński Waldemar, 2015. "Classification Problems Based on Regression Models for Multi-Dimensional Functional Data," Statistics in Transition New Series, Statistics Poland, vol. 16(1), pages 97-110, March.
    • Handle: RePEc:vrs:stintr:v:16:y:2015:i:1:p:97-110:n:5
    DOI: 10.21307/stattrans-2015-006
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    References listed on IDEAS

    as
    1. Ferraty, Frédéric & Vieu, Philippe, 2009. "Additive prediction and boosting for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1400-1413, February.
    2. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    3. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    4. Reiss, Philip T. & Ogden, R. Todd, 2007. "Functional Principal Component Regression and Functional Partial Least Squares," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 984-996, September.
    Full references (including those not matched with items on IDEAS)

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