IDEAS home Printed from https://ideas.repec.org/p/aiz/louvad/2011020.html
   My bibliography  Save this paper

Using Bagidis in nonparametric functional data analysis: predicting from curves with sharp local features

Author

Listed:
  • Timmermans, Catherine
  • Delsol, Laurent
  • von Sachs, Rainer

Abstract

No abstract is available for this item.

Suggested Citation

  • Timmermans, Catherine & Delsol, Laurent & von Sachs, Rainer, 2011. "Using Bagidis in nonparametric functional data analysis: predicting from curves with sharp local features," LIDAM Discussion Papers ISBA 2011020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2011020
    as

    Download full text from publisher

    File URL: https://cdn.uclouvain.be/public/Exports%20reddot/stat/documents/ISBADP2011-20_Using_Bagadis_in_nonparametric_functional_data_analysis...(2).pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aneiros-Pérez, Germán & Vieu, Philippe, 2008. "Nonparametric time series prediction: A semi-functional partial linear modeling," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 834-857, May.
    2. Florent Burba & Frédéric Ferraty & Philippe Vieu, 2009. "-Nearest Neighbour method in functional nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 453-469.
    3. Fryzlewicz, Piotr, 2007. "Unbalanced Haar technique for nonparametric function estimation," LSE Research Online Documents on Economics 25216, London School of Economics and Political Science, LSE Library.
    4. Timmermans, Catherine & von Sachs, Rainer, 2010. "BAGIDIS, a new method for statistical analysis of differences between curves with sharp discontinuities," LIDAM Discussion Papers ISBA 2010030, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Fryzlewicz, Piotr, 2007. "Unbalanced Haar Technique for Nonparametric Function Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1318-1327, December.
    6. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.
    7. Marron, James Stephen & Härdle, Wolfgang, 1986. "Random approximations to some measures of accuracy in nonparametric curve estimation," Journal of Multivariate Analysis, Elsevier, vol. 20(1), pages 91-113, October.
    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. Timmermans, Catherine & de Tullio, Pascal & Lambert, Vincent & Frederich, Michel & Rousseau, Rejane & von Sachs, Rainer, 2012. "Advantages of the Bagidis methodology for metabonomics analyses: application to a spectroscopic study of Age-related Macular Degeneration," LIDAM Discussion Papers ISBA 2012004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Timmermans, Catherine & Fryzlewicz, Piotr, 2012. "Shah: Shape-Adaptive Haar Wavelet Transform For Images With Application To Classification," LIDAM Discussion Papers ISBA 2012015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    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. Timmermans, Catherine & Delsol, Laurent & von Sachs, Rainer, 2013. "Using Bagidis in nonparametric functional data analysis: Predicting from curves with sharp local features," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 421-444.
    2. Schroeder, Anna Louise & Fryzlewicz, Piotr, 2013. "Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery," LSE Research Online Documents on Economics 54934, London School of Economics and Political Science, LSE Library.
    3. Aurore Delaigle & Peter Hall & Tung Pham, 2019. "Clustering functional data into groups by using projections," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 271-304, April.
    4. Chau, Van Vinh & von Sachs, Rainer, 2018. "Intrinsic wavelet regression for surfaces of Hermitian positive definite matrices," LIDAM Discussion Papers ISBA 2018025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Han Shang, 2014. "Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density," Computational Statistics, Springer, vol. 29(3), pages 829-848, June.
    6. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    7. Fryzlewicz, Piotr & Ombao, Hernando, 2009. "Consistent classification of non-stationary time series using stochastic wavelet representations," LSE Research Online Documents on Economics 25162, London School of Economics and Political Science, LSE Library.
    8. McGinnity, K. & Varbanov, R. & Chicken, E., 2017. "Cross-validated wavelet block thresholding for non-Gaussian errors," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 127-137.
    9. Homesh Sayal & John A. D. Aston & Duncan Elliott & Hernando Ombao, 2017. "An introduction to applications of wavelet benchmarking with seasonal adjustment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 863-889, June.
    10. Timmermans, Catherine & de Tullio, Pascal & Lambert, Vincent & Frederich, Michel & Rousseau, Rejane & von Sachs, Rainer, 2012. "Advantages of the Bagidis methodology for metabonomics analyses: application to a spectroscopic study of Age-related Macular Degeneration," LIDAM Discussion Papers ISBA 2012004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Chau, Joris & von Sachs, Rainer, 2022. "Time-varying spectral matrix estimation via intrinsic wavelet regression for surfaces of Hermitian positive definite matrices," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    12. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    13. 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.
    14. Shang, Han Lin & Hyndman, Rob.J., 2011. "Nonparametric time series forecasting with dynamic updating," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1310-1324.
    15. Ashish Sood & Gerard J. Tellis, 2009. "Do Innovations Really Pay Off? Total Stock Market Returns to Innovation," Marketing Science, INFORMS, vol. 28(3), pages 442-456, 05-06.
    16. Ashish Sood & V Kumar, 2018. "Client profitability of diffusion segments across countries for multi-generational innovations: The influence of firm, market, and cross-national differences," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 49(9), pages 1237-1262, December.
    17. Przystalski, Marcin, 2014. "Estimation of the covariance matrix in multivariate partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 380-385.
    18. Han Lin Shang & Yang Yang, 2021. "Forecasting Australian subnational age-specific mortality rates," Journal of Population Research, Springer, vol. 38(1), pages 1-24, March.
    19. Dabo-Niang, S. & Guillas, S. & Ternynck, C., 2016. "Efficiency in multivariate functional nonparametric models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 168-182.
    20. Blanquero, R. & Carrizosa, E. & Jiménez-Cordero, A. & Martín-Barragán, B., 2019. "Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm," European Journal of Operational Research, Elsevier, vol. 275(1), pages 195-207.

    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:aiz:louvad:2011020. 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: Nadja Peiffer (email available below). General contact details of provider: https://edirc.repec.org/data/isuclbe.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.