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2017 | 18 | nr 3 | 433--442
Tytuł artykułu

An Application of Functional Multivariate Regression Model to Multiclass Classification

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed method for classification for functional data. (original abstract)
Rocznik
Tom
18
Numer
Strony
433--442
Opis fizyczny
Twórcy
  • The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Poland
  • Adam Mickiewicz University in Poznań, Poland
Bibliografia
  • BACHE, K., LICHMAN, M., (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science (http://archive.ics.uci.edu/ml).
  • BERRENDERO, J. R., JUSTEL, A., SVARC, M., (2011). Principal Components for Multivariate Functional Data. Computational Statistics & Data Analysis, 55,2619-2634.
  • COLLAZOS, J. A. A., DIAS, R., ZAMBOM, A. Z., (2016). Consistent Variable Selection for Functional Regression Models. Journal of Multivariate Analysis, 146, 63-71.
  • FERRATY, F., VIEU, P., (2006). Nonparametric Functional Data Analysis: Theory and Practice, New York: Springer.
  • GÓRECKI, T., KRZYSKO, M., WASZAK, Ł., WOŁYNSKI, W., (2016). Selected Statistical Methods of Data Analysis for Multivariate Functional Data. Statistical Papers (Accepted) doi:10.1007/s00362-016-0757-8.
  • GÓRECKI, T., KRZYSKO, M., WOŁYNSKI, W., (2015). Classification Problem Based on Regression Models for Multidimensional Functional Data. Statistics in Transition new series, 16, 97-110.
  • GÓRECKI, T., SMAGA, Ł., (2017). Multivariate Analysis of Variance for Functional Data. Journal of Applied Statistics, 44, 2172-2189.
  • HORVÄTH, L., KOKOSZKA, P., (2012). Inference for Functional Data with Applications, New York: Springer.
  • JACQUES, J., PREDA, C., (2014). Model-Based Clustering for Multivariate Functional Data. Computational Statistics & Data Analysis, 71, 92-106.
  • KAYANO, M., KONISHI, S., (2009). Functional Principal Component Analysis via Regularized Gaussian Basis Expansions and its Application to Unbalanced Data. Journal of Statistical Planning and Inference, 139, 2388-2398.
  • KRZYSKO, M., WASZAK, Ł., (2013). Canonical Correlation Analysis for Functional Data. Biometrical Letters, 50, 95-105.
  • KRZYSKO, M., WOŁYŃSKI, W., GÓRECKI, T., SKORZYBUT, M., (2008). Learning Systems, Warsaw: WNT (in Polish).
  • MATSUI, H., (2014). Variable and Boundary Selection for Functional Data via Multiclass Logistic Regression Modeling. Computational Statistics & Data Analysis, 78, 176-185.
  • MATSUI, H., KONISHI, S., (2011). Variable Selection for Functional Regression Models via the L1 Regularization. Computational Statistics & Data Analysis, 55,3304-3310.
  • OLSZEWSKI, R. T., (2001). Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA (http://www.cs.cmu.edu/bobski).
  • RAMSAY, J. O., SILVERMAN, B. W., (2005). Functional Data Analysis, Second Edition, New York: Springer.
  • R DEVELOPMENT CORE TEAM, (2015). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (https://www.R-project.org/).
  • RODRIGUEZ, J. J., ALONSO, C. J., MAESTRO, J. A., (2005). Support Vector Machines of Interval Based Features for Time Series Classification. Knowledge-Based Systems, 18, 171-178.
  • SHMUELI, G., (2010). To Explain or to Predict? Statistical Science, 25, 289-310.
  • ZHANG, J.-T., (2013). Analysis of Variance for Functional Data, London: Chapman & Hall.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171499308

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