IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v37y2022i3d10.1007_s00180-021-01161-9.html
   My bibliography  Save this article

A Bayesian approach to the analysis of asymmetric association for two-way contingency tables

Author

Listed:
  • Zheng Wei

    (University of Maine)

  • Daeyoung Kim

    (University of Massachusetts)

  • Erin M. Conlon

    (University of Massachusetts)

Abstract

Recently, a subcopula-based asymmetric association measure was developed for the variables in two-way contingency tables. Here, we develop a fully Bayesian method to implement this measure, and examine its performance using simulation data and several real data sets of colorectal cancer. We use coverage probabilities and lengths of the interval estimators to compare the Bayesian approach and a large-sample method of analysis. In simulation studies, we find that the Bayesian method outperforms the large-sample method on average, and provides either similar or improved results for the real data analyses.

Suggested Citation

  • Zheng Wei & Daeyoung Kim & Erin M. Conlon, 2022. "A Bayesian approach to the analysis of asymmetric association for two-way contingency tables," Computational Statistics, Springer, vol. 37(3), pages 1311-1338, July.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01161-9
    DOI: 10.1007/s00180-021-01161-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-021-01161-9
    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/s00180-021-01161-9?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. Alan Agresti & David B. Hitchcock, 2005. "Bayesian inference for categorical data analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(3), pages 297-330, December.
    2. Malay Ghosh & Li Zahng & Bhramar Mukherjee, 2006. "Equivalence of posteriors in the Bayesian analysis of the multinomial-Poisson transformation," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 19-28.
    3. Leena Choi & Jeffrey D Blume & William D Dupont, 2015. "Elucidating the Foundations of Statistical Inference with 2 x 2 Tables," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-22, April.
    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. Roy Mill, 2011. "Hiring and Learning in Online Global Labor Markets," Working Papers 11-17, NET Institute, revised Oct 2011.
    2. Luca Greco & George Luta & Rand Wilcox, 2024. "On testing the equality between interquartile ranges," Computational Statistics, Springer, vol. 39(5), pages 2873-2898, July.
    3. Chen-Wei Liu & Björn Andersson & Anders Skrondal, 2020. "A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 322-357, June.
    4. Luai Al-Labadi & Petru Ciur & Milutin Dimovic & Kyuson Lim, 2023. "Assessing Multinomial Distributions with a Bayesian Approach," Mathematics, MDPI, vol. 11(13), pages 1-16, July.
    5. Wang, Y. & Daniels, M.J., 2013. "Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 130-140.
    6. Álvarez de Toledo, Pablo & Núñez, Fernando & Usabiaga, Carlos, 2020. "Matching in segmented labor markets: An analytical proposal based on high-dimensional contingency tables," Economic Modelling, Elsevier, vol. 93(C), pages 175-186.
    7. Dickhaus Thorsten, 2015. "Simultaneous Bayesian analysis of contingency tables in genetic association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 347-360, August.
    8. Xin Wang & Emily Berg & Zhengyuan Zhu & Dongchu Sun & Gabriel Demuth, 2018. "Small Area Estimation of Proportions with Constraint for National Resources Inventory Survey," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 509-528, December.
    9. Christoph Bartneck & Andreas Duenser & Elena Moltchanova & Karolina Zawieska, 2015. "Comparing the Similarity of Responses Received from Studies in Amazon’s Mechanical Turk to Studies Conducted Online and with Direct Recruitment," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
    10. Suesse Thomas & Namazi-Rad Mohammad-Reza & Mokhtarian Payam & Barthélemy Johan, 2017. "Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1021-1050, December.
    11. Shi Li & Bhramar Mukherjee & Stuart Batterman & Malay Ghosh, 2013. "Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference," Biometrics, The International Biometric Society, vol. 69(4), pages 925-936, December.
    12. Lei Shi & Hongyuan Sun & Peng Bai, 2009. "Bayesian confidence interval for difference of the proportions in a 2×2 table with structural zero," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 483-494.

    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:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01161-9. 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.