IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v14y2022i3d10.1007_s12561-022-09338-6.html
   My bibliography  Save this article

Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood

Author

Listed:
  • Bryan Ting

    (North Carolina State University)

  • Fred Wright

    (North Carolina State University
    North Carolina State University)

  • Yi-Hui Zhou

    (North Carolina State University
    North Carolina State University
    North Carolina State University)

Abstract

The multivariate probit is popular for modeling correlated binary data, with an attractive balance of flexibility and simplicity. However, considerable challenges remain in computation and in devising a clear statistical framework. Interest in the multivariate probit has increased in recent years. Current applications include genomics and precision medicine, where simultaneous modeling of multiple traits may be of interest, and computational efficiency is an important consideration. We propose a fast method for multivariate probit estimation via a two-stage composite likelihood. We explore computational and statistical efficiency, and note that the approach sets the stage for extensions beyond the purely binary setting.

Suggested Citation

  • Bryan Ting & Fred Wright & Yi-Hui Zhou, 2022. "Fast Multivariate Probit Estimation via a Two-Stage Composite Likelihood," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 533-549, December.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:3:d:10.1007_s12561-022-09338-6
    DOI: 10.1007/s12561-022-09338-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-022-09338-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-022-09338-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kuk, Anthony Y. C. & Nott, David J., 2000. "A pairwise likelihood approach to analyzing correlated binary data," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 329-335, May.
    2. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
    3. S. le Cessie & J. C. van Houwelingen, 1994. "Logistic Regression for Correlated Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 95-108, March.
    4. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    5. Joe, Harry & Lee, Youngjo, 2009. "On weighting of bivariate margins in pairwise likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 670-685, April.
    6. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    7. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    8. James W. Hardin, 2002. "The robust variance estimator for two-stage models," Stata Journal, StataCorp LP, vol. 2(3), pages 253-266, August.
    9. John Mullahy, 2016. "Estimation of multivariate probit models via bivariate probit," Stata Journal, StataCorp LP, vol. 16(1), pages 37-51, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Paleti, Rajesh & Bhat, Chandra R., 2013. "The composite marginal likelihood (CML) estimation of panel ordered-response models," Journal of choice modelling, Elsevier, vol. 7(C), pages 24-43.
    3. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    4. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2018. "Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach," Stata Journal, StataCorp LP, vol. 18(1), pages 206-222, March.
    5. Hyojoung Kim & Doyoung Kim & Subin Im & James W. Hardin, 2009. "Evidence of Asymmetric Information in the Automobile Insurance Market: Dichotomous Versus Multinomial Measurement of Insurance Coverage," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(2), pages 343-366, June.
    6. M.-L. Feddag, 2016. "Pairwise likelihood estimation for the normal ogive model with binary data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(2), pages 223-237, April.
    7. Renard, Didier & Molenberghs, Geert & Geys, Helena, 2004. "A pairwise likelihood approach to estimation in multilevel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 649-667, January.
    8. Haardt, David, 2006. "Transitions out of and back to employment among older men and women in the UK," ISER Working Paper Series 2006-20, Institute for Social and Economic Research.
    9. Trebesch, Christoph & Zabel, Michael, 2017. "The output costs of hard and soft sovereign default," European Economic Review, Elsevier, vol. 92(C), pages 416-432.
    10. Kaushal, Neeraj & Muchomba, Felix M., 2015. "How Consumer Price Subsidies affect Nutrition," World Development, Elsevier, vol. 74(C), pages 25-42.
    11. Mustafa U. Karakaplan & Levent Kutlu, 2017. "Handling Endogeneity in Stochastic Frontier Analysis," Economics Bulletin, AccessEcon, vol. 37(2), pages 889-901.
    12. Alwyn Young, 2012. "The African Growth Miracle," NBER Working Papers 18490, National Bureau of Economic Research, Inc.
    13. John Mullahy, 2017. "Marginal effects in multivariate probit models," Empirical Economics, Springer, vol. 52(2), pages 447-461, March.
    14. Joe, Harry & Lee, Youngjo, 2009. "On weighting of bivariate margins in pairwise likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 670-685, April.
    15. Felix M. Muchomba & Neeraj Kaushal, 2016. "Effect of Food Subsidies on Micronutrient Consumption," Indian Journal of Human Development, , vol. 10(3), pages 317-335, December.
    16. Cristiano PERUGINI & Fabrizio POMPEI, 2009. "Technological change and income distribution in Europe," International Labour Review, International Labour Organization, vol. 148(1-2), pages 123-148, June.
    17. Juan Muro & Cristina Suarez & Marıa del Mar Zamora, 2010. "Computing Murphy–Topel-corrected variances in a heckprobit model with endogeneity," Stata Journal, StataCorp LP, vol. 10(2), pages 252-258, June.
    18. Myrsini Katsikatsou & Irini Moustaki, 2016. "Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1046-1068, December.
    19. Atsushi Sekine & Takayuki Tsuruga, 2018. "Effects of commodity price shocks on inflation: a cross-country analysis," Oxford Economic Papers, Oxford University Press, vol. 70(4), pages 1108-1135.
    20. Büscher, Sebastian & Bauer, Dietmar, 2024. "Weighting strategies for pairwise composite marginal likelihood estimation in case of unbalanced panels and unaccounted autoregressive structure of the errors," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).

    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:spr:stabio:v:14:y:2022:i:3:d:10.1007_s12561-022-09338-6. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc 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 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.