Generalized copula-graphic estimator
Abstract
In this paper, a copula-graphic estimator is proposed for censored survival
data. It is assumed that there is some dependent censoring acting on the variable of
interest that may come from an existing competing risk. Furthermore, the full process
is independently censored by some administrative censoring time. The dependent
censoring is modeled through an Archimedean copula function, which is supposed to
be known. An asymptotic representation of the estimator as a sum of independent and
identically distributed random variables is obtained, and, consequently, a central limit
theorem is established. We investigate the finite sample performance of the estimator
through simulations. A real data illustration is included
URI
http://hdl.handle.net/10394/16667https://doi.org/10.1007/s11749-012-0314-2
https://link.springer.com/article/10.1007%2Fs11749-012-0314-2