When analyzing bivariate outcome data, it is often of scientific interest to measure and estimate the association between the bivariate outcomes. In the presence of influential covariates for one or both of the outcomes, conditional association measures can quantify the strength of association without the disturbance of the marginal covariate effects, to provide cleaner and less‐confounded insights into the bivariate association. In this work, we propose estimation and inferential procedures for assessing the conditional Kendall's tau coefficient given the covariates, by adopting the quantile regression and quantile copula framework to handle marginal covariate effects. The proposed method can flexibly accommodate right censoring and be readily applied to bivariate survival data. It also facilitates an estimator of the conditional concordance measure, namely, a conditional
- Award ID(s):
- 1916001
- NSF-PAR ID:
- 10237565
- Date Published:
- Journal Name:
- Canadian Journal of Statistics
- ISSN:
- 0319-5724
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Location Global.
Time period 2003–2015.
Major taxa studied Birds.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/cjs.11577
