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Wavelet-Based Clustering for Mixed-Effects Functional Models in High Dimension

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  • M. Giacofci
  • S. Lambert-Lacroix
  • G. Marot
  • F. Picard

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  • M. Giacofci & S. Lambert-Lacroix & G. Marot & F. Picard, 2013. "Wavelet-Based Clustering for Mixed-Effects Functional Models in High Dimension," Biometrics, The International Biometric Society, vol. 69(1), pages 31-40, March.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:1:p:31-40
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01828.x
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    References listed on IDEAS

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    1. Antoniadis, Anestis & Sapatinas, Theofanis, 2007. "Estimation and inference in functional mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4793-4813, June.
    2. Jeffrey S. Morris & Philip J. Brown & Richard C. Herrick & Keith A. Baggerly & Kevin R. Coombes, 2008. "Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models," Biometrics, The International Biometric Society, vol. 64(2), pages 479-489, June.
    3. Shubhankar Ray & Bani Mallick, 2006. "Functional clustering by Bayesian wavelet methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 305-332, April.
    4. Jeffrey S. Morris & Raymond J. Carroll, 2006. "Wavelet‐based functional mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 179-199, April.
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    Citations

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    Cited by:

    1. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    2. Adriano Zanin Zambom & Julian A. A. Collazos & Ronaldo Dias, 2019. "Functional data clustering via hypothesis testing k-means," Computational Statistics, Springer, vol. 34(2), pages 527-549, June.
    3. Maria Ruiz-Medina & Rosa Espejo & Elvira Romano, 2014. "Spatial functional normal mixed effect approach for curve classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 257-285, September.
    4. Chen, Di-Rong & Cheng, Kun & Liu, Chao, 2022. "Framelet block thresholding estimator for sparse functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    5. Dongik Jang & Hee-Seok Oh & Philippe Naveau, 2017. "Identifying local smoothness for spatially inhomogeneous functions," Computational Statistics, Springer, vol. 32(3), pages 1115-1138, September.
    6. Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Model-based Clustering of Count Processes," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 188-211, July.
    7. Kim, Joonpyo & Oh, Hee-Seok, 2020. "Pseudo-quantile functional data clustering," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    8. Matilde Trevisani & Arjuna Tuzzi, 2015. "A portrait of JASA: the History of Statistics through analysis of keyword counts in an early scientific journal," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1287-1304, May.
    9. Madison Giacofci & Sophie Lambert-Lacroix & Franck Picard, 2018. "Minimax wavelet estimation for multisample heteroscedastic nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 238-261, January.
    10. Chau, Van Vinh & von Sachs, Rainer, 2016. "Functional mixed effects wavelet estimation for spectra of replicated time series," LIDAM Discussion Papers ISBA 2016013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Ja‐Yoon Jang & Hee‐Seok Oh & Yaeji Lim & Ying Kuen Cheung, 2021. "Ensemble clustering for step data via binning," Biometrics, The International Biometric Society, vol. 77(1), pages 293-304, March.

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