Evidential-EM Algorithm Applied to Progressively Censored Observations

7 Jan 2015Kuang ZhouArnaud MartinQuan Pan

Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incom-plete data, where the loss of information is due to both mixture models and censored observations... (read more)

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