IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v30y2011i1p22-24.html

Commentary--A Latent Variable Perspective of Copula Modeling

Author

Listed:
  • Edward I. George

    (Department of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Shane T. Jensen

    (Department of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

The likelihood for copula modeling appears when both the data and the copula representations are seen as being driven by common uniform latent variables. This perspective facilitates Bayesian inference for prediction and copula selection.

Suggested Citation

  • Edward I. George & Shane T. Jensen, 2011. "Commentary--A Latent Variable Perspective of Copula Modeling," Marketing Science, INFORMS, vol. 30(1), pages 22-24, 01-02.
    • Handle: RePEc:inm:ormksc:v:30:y:2011:i:1:p:22-24
    DOI: 10.1287/mksc.1100.0579
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1100.0579
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.1100.0579?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
    ---><---

    References listed on IDEAS

    as
    1. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Peter J. Danaher & Michael S. Smith, 2011. "Rejoinder--Estimation Issues for Copulas Applied to Marketing Data," Marketing Science, INFORMS, vol. 30(1), pages 25-28, 01-02.

    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. Sungho Park & Sachin Gupta, 2012. "Handling Endogenous Regressors by Joint Estimation Using Copulas," Marketing Science, INFORMS, vol. 31(4), pages 567-586, July.
    2. Ceballos, Francisco, 2016. "Estimating spatial basis risk in rainfall index insurance: Methodology and application to excess rainfall insurance in Uruguay," IFPRI discussion papers 1595, International Food Policy Research Institute (IFPRI).
    3. Göran Kauermann & Renate Meyer, 2014. "Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas," Computational Statistics, Springer, vol. 29(1), pages 283-306, February.
    4. Tran, Kien C. & Tsionas, Efthymios G., 2015. "Endogeneity in stochastic frontier models: Copula approach without external instruments," Economics Letters, Elsevier, vol. 133(C), pages 85-88.
    5. Vernic, Raluca, 2018. "On the evaluation of some multivariate compound distributions with Sarmanov’s counting distribution," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 184-193.
    6. Stöber, Jakob & Hong, Hyokyoung Grace & Czado, Claudia & Ghosh, Pulak, 2015. "Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 28-39.
    7. repec:hum:wpaper:sfb649dp2013-041 is not listed on IDEAS
    8. Rouven E. Haschka & Helmut Herwartz, 2022. "Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 942-960, November.
    9. Yang, Yang & Ignatavičiūtė, Eglė & Šiaulys, Jonas, 2015. "Conditional tail expectation of randomly weighted sums with heavy-tailed distributions," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 20-28.
    10. Schröder, Nadine & Falke, Andreas & Hruschka, Harald & Reutterer, Thomas, 2019. "Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 181-197.
    11. Choudhary, Vidyanand & Currim, Imran & Dewan, Sanjeev & Jeliazkov, Ivan & Mintz, Ofer & Turner, John, 2017. "Evaluation Set Size and Purchase: Evidence from a Product Search Engine," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 16-31.
    12. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
    13. Göran Kauermann & Christian Schellhase & David Ruppert, 2013. "Flexible Copula Density Estimation with Penalized Hierarchical B-splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 685-705, December.
    14. Phoebe Koundouri & Nikolaos Englezos & Xanthi Kartala & Mike Tsionas, 2019. "A Decision-Analytic Framework to explore the water-energy-food nexus in complex and transboundary water resources systems, with Climate Change Uncertainty," DEOS Working Papers 1907, Athens University of Economics and Business.
    15. David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2024. "Bayesian Inference for Multidimensional Welfare Comparisons," Papers 2406.13395, arXiv.org.
    16. Lizhen Xu & Jason A. Duan & Andrew Whinston, 2014. "Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion," Management Science, INFORMS, vol. 60(6), pages 1392-1412, June.
    17. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    18. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    19. Karthik Sridhar & Ashish Kumar & Ram Bezawada, 2022. "Investigating cross-media effects in a multichannel marketing environment: the role of email, catalog, television, and radio," Marketing Letters, Springer, vol. 33(2), pages 189-201, June.
    20. Friberg, Richard & Huse, Cristian, 2012. "How to use demand systems to evaluate risky projects, with an application to automobile production," CEPR Discussion Papers 9266, C.E.P.R. Discussion Papers.
    21. Oliver J. Rutz & George F. Watson, 2019. "Endogeneity and marketing strategy research: an overview," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 479-498, May.
    22. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.

    More about this item

    Keywords

    ;
    ;
    ;

    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:inm:ormksc:v:30:y:2011:i:1:p:22-24. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.