Modelos de sobrevivência bivariados baseados na cópula PVF
Resumen
An alternative developed to study associations among multivariate survival times is the use of
models based on copula functions.
In this work, we use the survival model derived from the PVF copula, based on the Power
Variance Function distribution, to model the dependence of bivariate data in the presence of
covariates and censored observations. For inferential purposes, we perform a Bayesian approach
using Monte Carlo Markov Chain (MCMC) methods. Some discussions about model selection
criteria are presented. In order to detect influential observations, we used the Bayesian method
of deletion influence analysis of cases based on divergence psi. Finally, we show the applicability
of the proposed models to simulated and real datasets.
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