IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v70y2008i1p227-243.html
   My bibliography  Save this article

A new reconstruction of multivariate normal orthant probabilities

Author

Listed:
  • Peter Craig

Abstract

Summary. A new method is introduced for geometrically reconstructing orthant probabilities for non‐singular multivariate normal distributions. Orthant probabilities are expressed in terms of those for auto‐regressive sequences and an efficient method is developed for numerical approximation of the latter. The approach allows more efficient accurate evaluation of the multivariate normal cumulative distribution function than previously, for many situations where the original distribution arises from a graphical model. An implementation is available as a package for the statistical software R and an application is given to multivariate probit models.

Suggested Citation

  • Peter Craig, 2008. "A new reconstruction of multivariate normal orthant probabilities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 227-243, February.
    • Handle: RePEc:bla:jorssb:v:70:y:2008:i:1:p:227-243
    DOI: 10.1111/j.1467-9868.2007.00625.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2007.00625.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2007.00625.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Dubey, Subodh & Bansal, Prateek & Daziano, Ricardo A. & Guerra, Erick, 2020. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 114-141.
    2. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    3. Z. I. Botev, 2017. "The normal law under linear restrictions: simulation and estimation via minimax tilting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 125-148, January.
    4. Harrison-Trainor, Matthew, 2022. "An analysis of random elections with large numbers of voters," Mathematical Social Sciences, Elsevier, vol. 116(C), pages 68-84.
    5. Blake, Miranda R. & Dubey, Subodh & Swait, Joffre & Lancsar, Emily & Ghijben, Peter, 2020. "An integrated modelling approach examining the influence of goals, habit and learning on choice using visual attention data," Journal of Business Research, Elsevier, vol. 117(C), pages 44-57.
    6. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
    7. Jochen Ranger & Jörg-Tobias Kuhn, 2013. "Analyzing Response Times in Tests With Rank Correlation Approaches," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 61-80, February.
    8. Matthew Harrison-Trainor, 2020. "An Analysis of Random Elections with Large Numbers of Voters," Papers 2009.02979, arXiv.org.
    9. Jietao Xie & Juan Wu, 2020. "Recursive Calculation Model for a Special Multivariate Normal Probability of First-Order Stationary Sequence," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 164-171, January.
    10. Jacques Huguenin & Florian Pelgrin & Alberto Holly, 2009. "Estimation of multivariate probit models by exact maximum likelihood," Working Papers 0902, University of Lausanne, Institute of Health Economics and Management (IEMS).
    11. Subodh Dubey & Prateek Bansal & Ricardo A. Daziano & Erick Guerra, 2019. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Papers 1904.08332, arXiv.org, revised Jan 2020.
    12. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.

    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:bla:jorssb:v:70:y:2008:i:1:p:227-243. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.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.