IDEAS home Printed from https://ideas.repec.org/p/uts/rpaper/407.html
   My bibliography  Save this paper

An Application of High-Dimensional Statistics to Predictive Modeling of Grade Variability

Author

Listed:
  • Juri Hinz
  • Igor Grigoryev
  • Alexander Novikov

Abstract

The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach.

Suggested Citation

  • Juri Hinz & Igor Grigoryev & Alexander Novikov, 2020. "An Application of High-Dimensional Statistics to Predictive Modeling of Grade Variability," Research Paper Series 407, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:407
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-3263/10/4/116/pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    prediction; artificial intelligence; machine learning; LASSO; cross-validation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:uts:rpaper:407. 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: Duncan Ford (email available below). General contact details of provider: https://edirc.repec.org/data/qfutsau.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.