Parameter estimation for the generalized exponential distribution in the presence of interval censored data and covariate

This paper concentrates on the parameter estimation for the Generalized Exponential Distribution (GED) in the presence of interval-censored data with covariate. Interval-censored data usually arises in clinical and epidemiological studies. This research attempts to investigate a conservative imputation technique to deal with interval censored data. This is achieved by comparing the bias, standard error (SE) and root mean square error (RMSE) of the maximum likelihood estimation (MLE) obtained without using imputation with the proposed imputation method at various censoring proportion and sample sizes. The results indicate that the proposed imputation method performs better than the traditional method at all sample sizes and censoring proportions.

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