Assessing the goodness of fit of the Gompertz model in the presence of right and interval censored data with covariate

This research focuses on assessing the goodness of fit for the Gompertz model in the presence of right and interval censored data with covariate. The performance of the maximum likelihood estimates was evaluated via a simulation study at various censoring proportions and sample sizes. The conclusions were drawn based on the results of bias, standard error and root mean square error at different settings. Following that, another simulation study was carried out to compare the performance of the proposed modifications to the Cox-Snell residuals for both censored and uncensored observations at different combinations of sample sizes and censoring levels. The results show that standard error and root mean square error values of the parameter estimates increase with the increase in censoring proportions and decrease in the number of sample size. This indicates that the estimates perform better when sample sizes are larger and censoring proportions are lower. The performance of the proposed modifications of the Cox-Snell residuals showed that they perform slightly better than existing method.

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