IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02000704.html
   My bibliography  Save this paper

Approximate NORTA simulations for virtual sample generation

Author

Listed:
  • Guillaume Coqueret

    (MRM - Montpellier Research in Management - UPVM - Université Paul-Valéry - Montpellier 3 - UPVD - Université de Perpignan Via Domitia - Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School - UM - Université de Montpellier)

Abstract

We introduce an approximate variant of the NORTA method which aims at generating structured data from a given prior sample. The technique accommodates for any combinations of marginals (especially continuous/discrete mixtures) and a wide range of correlation structures. We focus on the interesting case where the sample includes categorical data, both ordered and unordered. We provide an application in the financial industry through a test of our iterative Newton-like algorithm on a dataset comprising the results of a questionnaire. We show that the sampled data, similarly to the NORTA technique, matches both the marginal and correlation structures of the original dataset closely. Consequently, analyses such as decision tree modeling or Support Vector Machine classification and regression, can be carried out on the new, much larger, sample without altering the core properties of the original sample.

Suggested Citation

  • Guillaume Coqueret, 2017. "Approximate NORTA simulations for virtual sample generation," Post-Print hal-02000704, HAL.
    • Handle: RePEc:hal:journl:hal-02000704
    DOI: 10.1016/j.eswa.2016.12.027
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:hal:journl:hal-02000704. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.