渐进删失观测值的证据EM算法
Evidential-EM Algorithm Applied to Progressively Censored Observations
摘要：
证据EM（E2M）算法是计算有限混合模型下极大似然估计的一种有效方法，特别是当数据具有不确定性信息时。在本文中，我们提出了一个E2M方法在一个特殊的情况下的不完全数据，其中的信息损失是由于混合模型和截尾观测。先验不确定性信息用置信函数表示，而伪似然函数是基于不精确观测和先验知识推导的。然后引入E2M方法，使广义似然函数最大化，得到参数的最优估计。数值算例表明，该方法能有效地将不确定的先验信息与观测数据所传递的当前不精确知识相结合.
英文摘要：
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.
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