Maximum likelihood estimation under a finite mixture of generalized exponential distributions based on censored data

In this paper, the identifiability of a finite mixture of generalized exponential distributions (GE(tau, alpha)) is proved and the maximum likelihood estimates (MLE's) of the parameters are obtained using EM algorithm based on a general form of right-censored failure times. The results are specialized to type-I and type-II censored samples. A real data set is introduced and analyzed using a mixture of two GE(tau, alpha) distributions and also using a mixture of two Weibull(alpha, beta) distributions. A comparison is carried out between the mentioned mixtures based on the corresponding Kolmogorov-Smirnov (K-S) test statistic to emphasize that the GE(tau, alpha) mixture model fits the data better than the other mixture model.