IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v22y2007i3p449-465.html
   My bibliography  Save this article

Computational considerations in functional principal component analysis

Author

Listed:
  • Francisco Ocaña
  • Ana Aguilera
  • Manuel Escabias

Abstract

No abstract is available for this item.

Suggested Citation

  • Francisco Ocaña & Ana Aguilera & Manuel Escabias, 2007. "Computational considerations in functional principal component analysis," Computational Statistics, Springer, vol. 22(3), pages 449-465, September.
    • Handle: RePEc:spr:compst:v:22:y:2007:i:3:p:449-465
    DOI: 10.1007/s00180-007-0051-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-007-0051-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-007-0051-2?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.

    References listed on IDEAS

    as
    1. John A. Rice & Colin O. Wu, 2001. "Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves," Biometrics, The International Biometric Society, vol. 57(1), pages 253-259, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. van der Linde, Angelika, 2008. "Variational Bayesian functional PCA," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 517-533, December.
    2. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    3. Francesca Fortuna & Fabrizio Maturo, 2019. "K-means clustering of item characteristic curves and item information curves via functional principal component analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2291-2304, September.
    4. Christian Acal & Ana M. Aguilera & Manuel Escabias, 2020. "New Modeling Approaches Based on Varimax Rotation of Functional Principal Components," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    5. Marc Vidal & Mattia Rosso & Ana M. Aguilera, 2021. "Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    6. Lakraj, Gamage Pemantha & Ruymgaart, Frits, 2017. "Some asymptotic theory for Silverman’s smoothed functional principal components in an abstract Hilbert space," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 122-132.
    7. M. Aguilera-Morillo & Ana Aguilera & Manuel Escabias & Mariano Valderrama, 2013. "Penalized spline approaches for functional logit regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 251-277, June.
    8. Ana M. Aguilera & Manuel Escabias & Francisco A. Ocaña & Mariano J. Valderrama, 2015. "Functional Wavelet-Based Modelling of Dependence Between Lupus and Stress," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 1015-1028, December.
    9. Ana M. Aguilera, 2016. "Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 23-26, March.
    10. Aguilera, Ana M. & Acal, Christian & Aguilera-Morillo, M. Carmen & Jiménez-Molinos, Francisco & Roldán, Juan B., 2021. "Homogeneity problem for basis expansion of functional data with applications to resistive memories," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 41-51.
    11. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2008. "Discussion of different logistic models with functional data. Application to Systemic Lupus Erythematosus," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 151-163, September.
    12. Manuel Escabias & Ana Aguilera & M. Aguilera-Morillo, 2014. "Functional PCA and Base-Line Logit Models," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 296-324, October.
    13. Aguilera-Morillo, M. Carmen & Aguilera, Ana M. & Jiménez-Molinos, Francisco & Roldán, Juan B., 2019. "Stochastic modeling of Random Access Memories reset transitions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 197-209.
    14. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2008. "Forecasting binary longitudinal data by a functional PC-ARIMA model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3187-3197, February.
    15. Ana Aguilera, 2016. "Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 23-26, March.
    16. Fabrizio Maturo & Rosanna Verde, 2023. "Supervised classification of curves via a combined use of functional data analysis and tree-based methods," Computational Statistics, Springer, vol. 38(1), pages 419-459, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jaroslaw Harezlak & Louise M. Ryan & Jay N. Giedd & Nicholas Lange, 2005. "Individual and Population Penalized Regression Splines for Accelerated Longitudinal Designs," Biometrics, The International Biometric Society, vol. 61(4), pages 1037-1048, December.
    2. Ye, Mao & Lu, Zhao-Hua & Li, Yimei & Song, Xinyuan, 2019. "Finite mixture of varying coefficient model: Estimation and component selection," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 452-474.
    3. Wei Liu & Lang Wu, 2007. "Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses," Biometrics, The International Biometric Society, vol. 63(2), pages 342-350, June.
    4. Bruno Scarpa & David B. Dunson, 2014. "Enriched Stick-Breaking Processes for Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 647-660, June.
    5. Huaihou Chen & Yuanjia Wang, 2011. "A Penalized Spline Approach to Functional Mixed Effects Model Analysis," Biometrics, The International Biometric Society, vol. 67(3), pages 861-870, September.
    6. Chen, Ziqi & Hu, Jianhua & Zhu, Hongtu, 2020. "Surface functional models," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    7. Shuang Wu & Hans-Georg Müller, 2011. "Response-Adaptive Regression for Longitudinal Data," Biometrics, The International Biometric Society, vol. 67(3), pages 852-860, September.
    8. Park, Yeonjoo & Simpson, Douglas G., 2019. "Robust probabilistic classification applicable to irregularly sampled functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 37-49.
    9. 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.
    10. Botts, Carsten H. & Daniels, Michael J., 2008. "A flexible approach to Bayesian multiple curve fitting," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5100-5120, August.
    11. Xinyue Chang & Yehua Li & Yi Li, 2023. "Asynchronous and error‐prone longitudinal data analysis via functional calibration," Biometrics, The International Biometric Society, vol. 79(4), pages 3374-3387, December.
    12. Nicoleta Serban & Ana-Maria Staicu & Raymond J. Carroll, 2013. "Multilevel Cross-Dependent Binary Longitudinal Data," Biometrics, The International Biometric Society, vol. 69(4), pages 903-913, December.
    13. Daowen Zhang & Xihong Lin & MaryFran Sowers, 2007. "Two-Stage Functional Mixed Models for Evaluating the Effect of Longitudinal Covariate Profiles on a Scalar Outcome," Biometrics, The International Biometric Society, vol. 63(2), pages 351-362, June.
    14. Fu, Eric & Heckman, Nancy, 2019. "Model-based curve registration via stochastic approximation EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 159-175.
    15. J. Goldsmith & S. Greven & C. Crainiceanu, 2013. "Corrected Confidence Bands for Functional Data Using Principal Components," Biometrics, The International Biometric Society, vol. 69(1), pages 41-51, March.
    16. Lihui Zhao & Tom Chen & Vladimir Novitsky & Rui Wang, 2021. "Joint penalized spline modeling of multivariate longitudinal data, with application to HIV‐1 RNA load levels and CD4 cell counts," Biometrics, The International Biometric Society, vol. 77(3), pages 1061-1074, September.
    17. Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 121-142, April.
    18. Wong, Raymond K.W. & Zhang, Xiaoke, 2019. "Nonparametric operator-regularized covariance function estimation for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 131-144.
    19. Ming Xiong & Ao Yuan & Hong-Bin Fang & Colin O. Wu & Ming T. Tan, 2022. "Estimation and Hypothesis Test for Mean Curve with Functional Data by Reproducing Kernel Hilbert Space Methods, with Applications in Biostatistics," Mathematics, MDPI, vol. 10(23), pages 1-17, December.
    20. Hao Ji & Hans-Georg Müller, 2017. "Optimal designs for longitudinal and functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 859-876, June.

    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:spr:compst:v:22:y:2007:i:3:p:449-465. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.