Advanced Search
MyIDEAS: Login to save this article or follow this journal

Smoothing signals for semimartingales

Contents:

Author Info

  • Thavaneswaran, A.
Registered author(s):

    Abstract

    The kernel function and convolution-smoothing methods developed to estimate a probability density function and distribution are essentially a way of smoothing the empirical distribution function. This paper shows now one can generalize these methods to estimate signals for a semimartingale model. A convolution-smoothed estimate is used to obtain an absolutely continuous estimate for an absolutely continuous signal of a semimartingale model. This provides a method of obtaining a convolution-smoothed estimate of the cumulative hazard function in the censored case, an open problem proposed by Mack (Bulletin of Informatics and Cybernetics 21 (1984) 29-35). Asymptotic properties of the convolution-smoothed estimate are discussed in some detail.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.sciencedirect.com/science/article/B6V1B-45FKRK0-11/2/4e17c80f0b5d853b277c70a8d1daae31
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Elsevier in its journal Stochastic Processes and their Applications.

    Volume (Year): 28 (1988)
    Issue (Month): 1 (April)
    Pages: 81-89

    as in new window
    Handle: RePEc:eee:spapps:v:28:y:1988:i:1:p:81-89

    Contact details of provider:
    Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description

    Order Information:
    Postal: http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
    Web: https://shop.elsevier.com/OOC/InitController?id=505572&ref=505572_01_ooc_1&version=01

    Related research

    Keywords: convolution-smoothing kernel functions semimartingales signals smoothing;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

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

    Cited by:
    1. A. Thavaneswaran & Jagbir Singh, 1993. "A note on smoothed estimating functions," Annals of the Institute of Statistical Mathematics, Springer, vol. 45(4), pages 721-729, December.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:spapps:v:28:y:1988:i:1:p:81-89. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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