IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v51y2007i12p5471-5476.html
   My bibliography  Save this article

Simulating from a multinomial distribution with large number of categories

Author

Listed:
  • Malefaki, Sonia
  • Iliopoulos, George

Abstract

No abstract is available for this item.

Suggested Citation

  • Malefaki, Sonia & Iliopoulos, George, 2007. "Simulating from a multinomial distribution with large number of categories," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5471-5476, August.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:5471-5476
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00140-5
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    2. repec:dau:papers:123456789/6072 is not listed on IDEAS
    3. Imai, Kosuke & van Dyk, David A., 2005. "A Bayesian analysis of the multinomial probit model using marginal data augmentation," Journal of Econometrics, Elsevier, vol. 124(2), pages 311-334, February.
    4. Davis, Charles S., 1993. "The computer generation of multinomial random variates," Computational Statistics & Data Analysis, Elsevier, vol. 16(2), pages 205-217, August.
    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. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.
    2. Robert Zeithammer & Peter Lenk, 2006. "Bayesian estimation of multivariate-normal models when dimensions are absent," Quantitative Marketing and Economics (QME), Springer, vol. 4(3), pages 241-265, September.
    3. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R., 2008. "Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3697-3708, March.
    4. Caffo, Brian & An, Ming-Wen & Rohde, Charles, 2007. "Flexible random intercept models for binary outcomes using mixtures of normals," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5220-5235, July.
    5. Raja Chakir & Olivier Parent, 2009. "Determinants of land use changes: A spatial multinomial probit approach," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 327-344, June.
    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. Ricardo A. Daziano & Martin Achtnicht, 2014. "Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator," Transportation Science, INFORMS, vol. 48(4), pages 671-683, November.
    8. Jianmei ZHAO, 2014. "Rural income diversification patterns and their determinants in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 60(5), pages 219-231.
    9. Chiew, Esther & Daziano, Ricardo A., 2016. "A Bayes multinomial probit model for random consumer-surplus maximization," Journal of choice modelling, Elsevier, vol. 21(C), pages 56-59.
    10. Ruben Loaiza-Maya & Didier Nibbering, 2020. "Scalable Bayesian estimation in the multinomial probit model," Papers 2007.13247, arXiv.org, revised Mar 2021.
    11. Piatek, Rémi & Gensowski, Miriam, 2017. "A Multinomial Probit Model with Latent Factors: Identification and Interpretation without a Measurement System," IZA Discussion Papers 11042, Institute of Labor Economics (IZA).
    12. Park, Sang Soo & Lee, Chung-Ki, 2011. "베이지안 추정법을 이용한 주택선택의 다항프로빗 모형 분석 [Analysis of housing choice using multinomial probit model – Bayesian estimation]," MPRA Paper 37150, University Library of Munich, Germany.
    13. Lamoureux, Christopher G. & Nejadmalayeri, Ali, 2015. "Costs of capital and public issuance choice," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 27-45.
    14. Harald Hruschka, 2007. "Using a heterogeneous multinomial probit model with a neural net extension to model brand choice," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 113-127.
    15. Rico Krueger & Michel Bierlaire & Thomas Gasos & Prateek Bansal, 2020. "Robust discrete choice models with t-distributed kernel errors," Papers 2009.06383, arXiv.org, revised Dec 2022.
    16. Berrett, Candace & Calder, Catherine A., 2012. "Data augmentation strategies for the Bayesian spatial probit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 478-490.
    17. Duncan Fong & Sunghoon Kim & Zhe Chen & Wayne DeSarbo, 2016. "A Bayesian Multinomial Probit MODEL FOR THE ANALYSIS OF PANEL CHOICE DATA," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 161-183, March.
    18. Veyssiere, Luc Pierre, 2009. "A three essays dissertation on agricultural and environmental microeconomics," ISU General Staff Papers 200901010800001958, Iowa State University, Department of Economics.
    19. Didier Nibbering, 2019. "A High-dimensional Multinomial Choice Model," Monash Econometrics and Business Statistics Working Papers 19/19, Monash University, Department of Econometrics and Business Statistics.
    20. Veettil, Prakashan Chellattan & Speelman, Stijn & Frija, Aymen & Buysse, Jeroen & van Huylenbroeck, Guido, 2011. "Complementarity between water pricing, water rights and local water governance: A Bayesian analysis of choice behaviour of farmers in the Krishna river basin, India," Ecological Economics, Elsevier, vol. 70(10), pages 1756-1766, August.

    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:eee:csdana:v:51:y:2007:i:12:p:5471-5476. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.