IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i1p163-172.html
   My bibliography  Save this article

The functional singular value decomposition for bivariate stochastic processes

Author

Listed:
  • Gervini, Daniel

Abstract

In this article we present some statistical applications of the functional singular value decomposition (FSVD). This tool allows us to decompose the sample mean of a bivariate stochastic process into components that are functions of separate variables. These components are sometimes interpretable functions that summarize salient features of the data. The FSVD can be used to visually detect outliers, to estimate the mean of a stochastic process or to obtain individual smoothers of the sample surfaces. As estimators of the mean, we show by simulation that FSVD estimators are competitive with tensor-product splines in some situations.

Suggested Citation

  • Gervini, Daniel, 2010. "The functional singular value decomposition for bivariate stochastic processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 163-172, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:163-172
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00269-2
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Niccol`o Ajroldi & Jacopo Diquigiovanni & Matteo Fontana & Simone Vantini, 2022. "Conformal Prediction Bands for Two-Dimensional Functional Time Series," Papers 2207.13656, arXiv.org, revised Jul 2023.

    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:eee:csdana:v:54:y:2010:i:1:p:163-172. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.