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

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Abstract

In this paper, the identifiability of a finite mixture of generalized exponential distributions (GE(τ, α)) 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(τ, α) distributions and also using a mixture of two Weibull(α, β) 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(τ, α) mixture model fits the data better than the other mixture model.

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Authors and Affiliations

  1. Department of Mathematics & Statistics, Faculty of Science, Taif University, Taif, Saudi Arabia

    Saieed F. Ateya

  2. Department of Mathematics, Faculty of Science, Assiut University, Assiut, Egypt

    Saieed F. Ateya

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  1. Saieed F. Ateya
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Correspondence to Saieed F. Ateya.

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Ateya, S.F. Maximum likelihood estimation under a finite mixture of generalized exponential distributions based on censored data. Stat Papers 55, 311–325 (2014). https://doi.org/10.1007/s00362-012-0480-z

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  • DOI: https://doi.org/10.1007/s00362-012-0480-z

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