ADDITIVE SHARED INVERSE GAUSSIAN FRAILTY MODEL

Abstract

The study proposes additive hazard shared inverse Gaussian frailty model with generalized Pareto, generalized Rayleigh and xgamma distributions as baseline distribution to analyze the bivariate data set of McGilchrist and Aisbett (1991). The estimation of the parameters involved in the models was done by Bayesian approach of Markov Chain Monte Carlo technique. The true values and the estimated values of the parameters are compared by using simulation study. The proposed models are fitted to the real life data set and the best model suggested for the data.