IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v186y2021ics0047259x21000713.html
   My bibliography  Save this article

On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables

Author

Listed:
  • Wei, Zheng
  • Kim, Daeyoung

Abstract

In this paper, we propose a new model-free exploratory method for descriptive modeling that identifies and measures the regression dependence between an ordinal response variable and categorical (ordinal or nominal) explanatory variables in a multi-way contingency table. The proposed methodology consists of three parts, checkerboard copula score, checkerboard copula regression, and checkerboard copula association measure. The checkerboard copula score is a new type of score for ordinal variables that preserves the natural ordering of the categorical scale and it will be exploited for developing the methods measuring the association between the variables of interest. The checkerboard copula regression identifies the regression dependence between an ordinal response variable and categorical explanatory variables. It enables delineating the identified dependence in an exploratory manner. The checkerboard copula association measure quantifies the strength of the dependence identified by the checkerboard copula regression. We investigate the properties of checkerboard copula scores, checkerboard copula regression, its association measure, and their estimators. Finally, the performance of the proposed method is illustrated with simulation and real data.

Suggested Citation

  • Wei, Zheng & Kim, Daeyoung, 2021. "On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
  • Handle: RePEc:eee:jmvana:v:186:y:2021:i:c:s0047259x21000713
    DOI: 10.1016/j.jmva.2021.104793
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X21000713
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jmva.2021.104793?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. Genest, Christian & Nešlehová, Johanna G. & Rémillard, Bruno, 2017. "Asymptotic behavior of the empirical multilinear copula process under broad conditions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 82-110.
    2. Lu Yang & Edward W. Frees & Zhengjun Zhang, 2020. "Nonparametric Estimation of Copula Regression Models With Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 707-720, April.
    3. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    4. Yee, Thomas W., 2010. "The VGAM Package for Categorical Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i10).
    5. Li, Chun & Shepherd, Bryan E., 2010. "Test of Association Between Two Ordinal Variables While Adjusting for Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 612-620.
    6. Denuit, Michel & Lambert, Philippe, 2005. "Constraints on concordance measures in bivariate discrete data," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 40-57, March.
    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. Wei, Zheng & Wang, Li & Liao, Shu-Min & Kim, Daeyoung, 2023. "On the exploration of regression dependence structures in multidimensional contingency tables with ordinal response variables," Journal of Multivariate Analysis, Elsevier, vol. 196(C).

    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. Wei, Zheng & Wang, Li & Liao, Shu-Min & Kim, Daeyoung, 2023. "On the exploration of regression dependence structures in multidimensional contingency tables with ordinal response variables," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    2. Geenens Gery, 2020. "Copula modeling for discrete random vectors," Dependence Modeling, De Gruyter, vol. 8(1), pages 417-440, January.
    3. L. L. Henn, 2022. "Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data," Computational Statistics, Springer, vol. 37(2), pages 909-946, April.
    4. Nagler, Thomas, 2018. "A generic approach to nonparametric function estimation with mixed data," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 326-330.
    5. Xiaotian Zheng & Athanasios Kottas & Bruno Sansó, 2023. "Bayesian geostatistical modeling for discrete‐valued processes," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    6. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    7. Jonas Moss & Steffen Grønneberg, 2023. "Partial Identification of Latent Correlations with Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 241-252, March.
    8. Tutz, G. & Berger, M., 2017. "Separating location and dispersion in ordinal regression models," Econometrics and Statistics, Elsevier, vol. 2(C), pages 131-148.
    9. Liu, Dungang & Li, Shaobo & Yu, Yan & Moustaki, Irini, 2020. "Assessing partial association between ordinal variables: quantification, visualization, and hypothesis testing," LSE Research Online Documents on Economics 105558, London School of Economics and Political Science, LSE Library.
    10. Geenens Gery, 2020. "Copula modeling for discrete random vectors," Dependence Modeling, De Gruyter, vol. 8(1), pages 417-440, January.
    11. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    12. Emanuela Raffinetti & Fabio Aimar, 2019. "MDCgo takes up the association/correlation challenge for grouped ordinal data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 527-561, December.
    13. Lu Yang & Claudia Czado, 2022. "Two‐part D‐vine copula models for longitudinal insurance claim data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1534-1561, December.
    14. Jiang, Xianfeng & Packer, Frank, 2019. "Credit ratings of Chinese firms by domestic and global agencies: Assessing the determinants and impact," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 178-193.
    15. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2008. "A multivariate integer count hurdle model: theory and application to exchange rate dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 31-48, Springer.
    16. Quinn C, 2009. "Measuring income-related inequalities in health using a parametric dependence function," Health, Econometrics and Data Group (HEDG) Working Papers 09/24, HEDG, c/o Department of Economics, University of York.
    17. Koen Decancq, 2014. "Copula-based measurement of dependence between dimensions of well-being," Oxford Economic Papers, Oxford University Press, vol. 66(3), pages 681-701.
    18. Bambio, Yiriyibin & Bouayad Agha, Salima, 2018. "Land tenure security and investment: Does strength of land right really matter in rural Burkina Faso?," World Development, Elsevier, vol. 111(C), pages 130-147.
    19. Eugenio J. Miravete, 2009. "Competing with Menus of Tariff Options," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 188-205, March.
    20. Mohamad Khaled & Paul Makdissi & Prasada Rao & Myra Yazbeck, 2023. "A Unidimensional Representation of Multidimensional Inequality: An Econometric Analysis of Inequalities in the Arab Region," Working Papers 2304E Classification- D63, University of Ottawa, Department of Economics.

    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:jmvana:v:186:y:2021:i:c:s0047259x21000713. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.