Regression in a copula model for bivariate count data

Aristidis Nikoloulopoulos and Dimitris Karlis

Journal of Applied Statistics, 2010, vol. 37, issue 9, 1555-1568

Abstract: In many cases of modeling bivariate count data, the interest lies on studying the association rather than the marginal properties. We form a flexible regression copula-based model where covariates are used not only for the marginal but also for the copula parameters. Since copula measures the association, the use of covariates in its parameters allow for direct modeling of association. A real-data application related to transaction market basket data is used. Our goal is to refine and understand whether the association between the number of purchases of certain product categories depends on particular demographic customers' characteristics. Such information is important for decision making for marketing purposes.

Keywords: dependence modeling; Kendall's tau; covariate function; negative binomial distribution (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760903093591 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:37:y:2010:i:9:p:1555-1568

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760903093591

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().