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Clustering multivariate functional data with phase variation

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  • Juhyun Park
  • Jeongyoun Ahn

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  • Juhyun Park & Jeongyoun Ahn, 2017. "Clustering multivariate functional data with phase variation," Biometrics, The International Biometric Society, vol. 73(1), pages 324-333, March.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:324-333
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    File URL: http://hdl.handle.net/10.1111/biom.12546
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    References listed on IDEAS

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    1. Sebastian Kurtek & Anuj Srivastava & Eric Klassen & Zhaohua Ding, 2012. "Statistical Modeling of Curves Using Shapes and Related Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1152-1165, September.
    2. Jeng‐Min Chiou & Pai‐Ling Li, 2007. "Functional clustering and identifying substructures of longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 679-699, September.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. Kneip, Alois & Ramsay, James O, 2008. "Combining Registration and Fitting for Functional Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1155-1165.
    5. Liu, Xueli & Yang, Mark C.K., 2009. "Simultaneous curve registration and clustering for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1361-1376, February.
    6. Tucker, J. Derek & Wu, Wei & Srivastava, Anuj, 2013. "Generative models for functional data using phase and amplitude separation," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 50-66.
    7. Daniel Gervini & Theo Gasser, 2004. "Self‐modelling warping functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 959-971, November.
    8. P. Z. Hadjipantelis & J. A. D. Aston & H. G. Müller & J. P. Evans, 2015. "Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 545-559, June.
    9. Slaets, Leen & Claeskens, Gerda & Hubert, Mia, 2012. "Phase and amplitude-based clustering for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2360-2374.
    10. Francesca Ieva & Anna M. Paganoni & Davide Pigoli & Valeria Vitelli, 2013. "Multivariate functional clustering for the morphological analysis of electrocardiograph curves," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 401-418, May.
    11. Xueli Liu & Hans-Georg Muller, 2004. "Functional Convex Averaging and Synchronization for Time-Warped Random Curves," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 687-699, January.
    12. Sangalli, Laura M. & Secchi, Piercesare & Vantini, Simone & Vitelli, Valeria, 2010. "k-mean alignment for curve clustering," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1219-1233, May.
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    Cited by:

    1. Francisco Martínez-Álvarez & Amandine Schmutz & Gualberto Asencio-Cortés & Julien Jacques, 2018. "A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand," Energies, MDPI, vol. 12(1), pages 1-18, December.
    2. Snježana Majstorović & Kristian Sabo & Johannes Jung & Matija Klarić, 2018. "Spectral methods for growth curve clustering," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 715-737, September.
    3. Kyunghee Han & Pantelis Z Hadjipantelis & Jane-Ling Wang & Michael S Kramer & Seungmi Yang & Richard M Martin & Hans-Georg Müller, 2018. "Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    4. Cody Carroll & Hans‐Georg Müller, 2023. "Latent deformation models for multivariate functional data and time‐warping separability," Biometrics, The International Biometric Society, vol. 79(4), pages 3345-3358, December.
    5. Cody Carroll & Hans‐Georg Müller & Alois Kneip, 2021. "Cross‐component registration for multivariate functional data, with application to growth curves," Biometrics, The International Biometric Society, vol. 77(3), pages 839-851, September.
    6. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).

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