IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v62y2013i3p401-418.html
   My bibliography  Save this article

Multivariate functional clustering for the morphological analysis of electrocardiograph curves

Author

Listed:
  • Francesca Ieva
  • Anna M. Paganoni
  • Davide Pigoli
  • Valeria Vitelli

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:62:y:2013:i:3:p:401-418
    DOI: j.1467-9876.2012.01062.x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1467-9876.2012.01062.x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/j.1467-9876.2012.01062.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jacopo Canello & Francesco Vidoli, 2020. "Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 653-676, September.
    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. O. I. Traore & P. Cristini & N. Favretto-Cristini & L. Pantera & P. Vieu & S. Viguier-Pla, 2019. "Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches," Computational Statistics, Springer, vol. 34(2), pages 631-652, June.
    4. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers 06/15, Institute for Fiscal Studies.
    5. Michael Vogt & Oliver Linton, 2015. "Classification of nonparametric regression functions in heterogeneous panels," CeMMAP working papers CWP06/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Li, Zehang & Elías, Antonio & Morales, Juan M., 2024. "Clustering and forecasting of day-ahead electricity supply curves using a market-based distance," DES - Working Papers. Statistics and Econometrics. WS 43805, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Juhyun Park & Jeongyoun Ahn, 2017. "Clustering multivariate functional data with phase variation," Biometrics, The International Biometric Society, vol. 73(1), pages 324-333, March.
    8. Amandine Schmutz & Julien Jacques & Charles Bouveyron & Laurence Chèze & Pauline Martin, 2020. "Clustering multivariate functional data in group-specific functional subspaces," Computational Statistics, Springer, vol. 35(3), pages 1101-1131, September.
    9. Kim, Joonpyo & Oh, Hee-Seok, 2020. "Pseudo-quantile functional data clustering," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    10. Francesca Ieva & Anna Paganoni, 2015. "Discussion of “multivariate functional outlier detection” by M. Hubert, P. Rousseeuw and P. Segaert," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 217-221, July.
    11. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    12. Marta Spreafico & Francesca Ieva & Marta Fiocco, 2023. "Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 271-298, March.
    13. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    14. Ana Justel & Marcela Svarc, 2018. "A divisive clustering method for functional data with special consideration of outliers," 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. 12(3), pages 637-656, September.
    15. Andrea Martino & Andrea Ghiglietti & Francesca Ieva & Anna Maria Paganoni, 2019. "A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 301-322, June.
    16. Michael Vogt & Oliver Linton, 2017. "Classification of non-parametric regression functions in longitudinal data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 5-27, January.
    17. Daewon Yang & Taeryon Choi & Eric Lavigne & Yeonseung Chung, 2022. "Non‐parametric Bayesian covariate‐dependent multivariate functional clustering: An application to time‐series data for multiple air pollutants," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1521-1542, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:62:y:2013:i:3:p:401-418. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.