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Detecting changes in the mean of functional observations

Author

Listed:
  • István Berkes
  • Robertas Gabrys
  • Lajos Horváth
  • Piotr Kokoszka

Abstract

Summary. Principal component analysis has become a fundamental tool of functional data analysis. It represents the functional data as Xi(t)=μ(t)+Σ1≤l

Suggested Citation

  • István Berkes & Robertas Gabrys & Lajos Horváth & Piotr Kokoszka, 2009. "Detecting changes in the mean of functional observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 927-946, November.
    • Handle: RePEc:bla:jorssb:v:71:y:2009:i:5:p:927-946
    DOI: 10.1111/j.1467-9868.2009.00713.x
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    References listed on IDEAS

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    1. Ferraty, Frederic & Vieu, Philippe & Viguier-Pla, Sylvie, 2007. "Factor-based comparison of groups of curves," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4903-4910, June.
    2. Laukaitis, Algirdas & Rackauskas, Alfredas, 2005. "Functional data analysis for clients segmentation tasks," European Journal of Operational Research, Elsevier, vol. 163(1), pages 210-216, May.
    3. Zhongjun Qu & Pierre Perron, 2007. "Estimating and Testing Structural Changes in Multivariate Regressions," Econometrica, Econometric Society, vol. 75(2), pages 459-502, March.
    4. Kuelbs, J., 1973. "The invariance principle for Banach space valued random variables," Journal of Multivariate Analysis, Elsevier, vol. 3(2), pages 161-172, June.
    5. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
    6. Gabrys, Robertas & Kokoszka, Piotr, 2007. "Portmanteau Test of Independence for Functional Observations," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1338-1348, December.
    7. Pedro Delicado, 2007. "Functional k-sample problem when data are density functions," Computational Statistics, Springer, vol. 22(3), pages 391-410, September.
    8. Antoniadis, Anestis & Sapatinas, Theofanis, 2003. "Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 133-158, October.
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