Computational Statistics & Data Analysis
Accelerating the convergence of the EM algorithm using the vector ε algorithm
Computational Statistics & Data Analysis
Acceleration of the EM algorithm using the vector epsilon algorithm
Computational Statistics
Parabolic acceleration of the EM algorithm
Statistics and Computing
Accelerating EM: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A multivariate linear regression analysis using finite mixtures of t distributions
Computational Statistics & Data Analysis
Improving mixture tree construction using better EM algorithms
Computational Statistics & Data Analysis
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Among recent methods designed for accelerating the EM algorithm without any modification in the structure of EM or in the statistical model, the parabolic acceleration (P-EM) has proved its efficiency. It does not involve any computation of gradient or hessian matrix and can be used as an additional software component of any fixed point algorithm maximizing some objective function. The vector epsilon algorithm was introduced to reach the same goals. Through geometric considerations, the relationships between the outputs of an improved version of P-EM and those of the vector epsilon algorithm are established. This sheds some light on their different behaviours and explains why the parabolic acceleration of EM outperforms its competitor in most numerical experiments. A detailed analysis of its trajectories in a variety of real or simulated data shows the ability of P-EM to choose the most efficient paths to the global maximum of the likelihood.