Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm
提出一种扩展的EM算法，在混合分布模型中恢复对数似然函数的可加可分性，从而允许在最大化步骤中顺序估计参数，相比全信息最大似然估计可大幅节省计算成本且效率损失很小。
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly: using mixture distributions destroys any additive separability of the log-likelihood function. We show, however, that an extension of the EM algorithm reintroduces additive separability, thus allowing one to estimate parameters sequentially during each maximization step. In establishing this result, we develop a broad class of estimators for mixture models. Returning to the likelihood problem, we show that, relative to full information maximum likelihood, our sequential estimator can generate large computational savings with little loss of efficiency.