The paper is framed within the literature around Louis’ identity for the observed information matrix in incomplete data problems, with a focus on the implied acceleration of maximum likelihood estimation for mixture models. The goal is twofold: to obtain direct expressions for standard errors of parameters from the EM algorithm and to reduce the computational burden of the estimation procedure for a class of mixture models with uncertainty for rating variables. This achievement fosters the feasibility of best-subset variable selection, which is an advisable strategy to identify response patterns from regression models for all Mixtures of Experts systems. The discussion is supported by simulation experiments and a real case study.

本文围绕不完整数据问题中观察到的信息矩阵的路易斯身份在文献中作了介绍，重点关注混合模型的最大似然估计的隐式加速。目标是双重的：从EM算法获得参数标准误差的直接表达式，并减少一类具有评估变量不确定性的混合模型的估计程序的计算负担。这一成就促进了最佳子集变量选择的可行性，这是从所有专家混合物系统的回归模型中识别响应模式的明智策略。讨论得到了模拟实验和实际案例的支持。