Evidential-EM Algorithm Applied to Progressively Censored Observations

TL;DR

This paper extends the Evidential-EM algorithm to handle incomplete data with censored observations, effectively integrating uncertain prior information with imprecise data to improve parameter estimation.

Contribution

It introduces an extension of the E2M method for censored data using belief functions and generalized likelihood, enhancing estimation under uncertainty.

Findings

Effective integration of prior uncertain information

Improved parameter estimation with censored data

Numerical examples demonstrate method's effectiveness

Abstract

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. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior infor-mation with the current imprecise knowledge conveyed by the observed data.