A common challenge in working with longitudinal data is dealing with incomplete data. According to the existing studies on the dependence structure of survival times, it is a riveting topic for researchers to estimate survival functions and dependence parameters, especially in biology and medical research. Some researchers have studied the aforementioned subjects with left- or right-truncated or censored data. When the data involves interval censoring, the mentioned issues still need to be solved or modified. In this article, we propose two alternative approaches to the estimation of a dependence parameter and Kendall's $\tau$, given an interesting covariate and interval-censored dataset. More precisely, these approaches include non-parametric and semi-parametric methods to estimate the copula dependence parameter and Kendall's τ, which are evaluated by simulation. Finally, we apply the mentioned approaches to a real-world dataset and copula's goodness-of-fit tests.
Ghanbari, F., & Dehghan, M. H. (2023). Estimating Kendall's τ when both times are subject to interval censoring. Journal of Statistical Modelling: Theory and Applications, 4(2), 97-110. doi: 10.22034/jsmta.2024.21276.1131
MLA
Fatemeh Ghanbari; Mohammad Hossein Dehghan. "Estimating Kendall's τ when both times are subject to interval censoring", Journal of Statistical Modelling: Theory and Applications, 4, 2, 2023, 97-110. doi: 10.22034/jsmta.2024.21276.1131
HARVARD
Ghanbari, F., Dehghan, M. H. (2023). 'Estimating Kendall's τ when both times are subject to interval censoring', Journal of Statistical Modelling: Theory and Applications, 4(2), pp. 97-110. doi: 10.22034/jsmta.2024.21276.1131
VANCOUVER
Ghanbari, F., Dehghan, M. H. Estimating Kendall's τ when both times are subject to interval censoring. Journal of Statistical Modelling: Theory and Applications, 2023; 4(2): 97-110. doi: 10.22034/jsmta.2024.21276.1131
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