Read our Editor's Choice Article "Stochastic Expectation Maximization Algorithm for Linear Mixed-Effects Model with Interactions in the Presence
Entropy MDPI
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Title: Stochastic Expectation Maximization Algorithm for Linear Mixed-Effects Model with Interactions in the Presence of Incomplete Data
Authors: Alandra Zakkour, Cyril Perret and Yousri Slaoui
Abstract: The purpose of this paper is to propose a new algorithm based on stochastic expectation maximization (SEM) to deal with the problem of unobserved values when multiple interactions in a linear mixed-effects model (LMEM) are present. We test the effectiveness of the proposed algorithm with the stochastic approximation expectation maximization (SAEM) and Monte Carlo Markov chain (MCMC) algorithms. This comparison is implemented to highlight the importance of including the maximum effects that can affect the model. The applications are made on both simulated psychological and real data. The findings demonstrate that our proposed SEM algorithm is highly preferable to the other competitor algorithms.
Read full article at: https://www.mdpi.com/1099-4300/25/3/473
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