linear mixed model based on mean mixture of multivariate normal distributions‎: ‎a flexible estimate based on missing value

The purpose of this paper is to extend the linear mixed model for handling missing and {heavy-tailed} data‎. ‎in this model‎, ‎the random effects have multivariate mean mixture of normal distribution and errors arise from a multivariate normal distribution‎. ‎an expectation conditional maximization algorithm is developed for parameter estimation based on missing information‎. ‎the mechanism of missing data is missing-at-random‎. ‎simulation studies and real data sets represent the efficiency and performance of the proposed model‎.