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Stochastic Expectation Maximization Algorithm for Linear Mixed-Effects Model with Interactions in the Presence of

Alandra Zakkour1,2, Cyril Perret1,2, Yousri Slaoui1

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A new stochastic expectation maximization (SEM) algorithm effectively handles missing data in linear mixed-effects models (LMEM). This advanced SEM approach outperforms stochastic approximation expectation maximization (SAEM) and Monte Carlo Markov chain (MCMC) methods.

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Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Missing data is a common challenge in statistical modeling.
  • Linear mixed-effects models (LMEM) are widely used in various fields, including psychology and medicine.
  • Handling unobserved values in LMEM with multiple interactions requires robust algorithms.

Purpose of the Study:

  • To introduce a novel algorithm based on stochastic expectation maximization (SEM) for addressing unobserved values in LMEM with multiple interactions.
  • To evaluate the performance of the proposed SEM algorithm against existing methods.
  • To emphasize the significance of incorporating maximum effects in statistical models.

Main Methods:

  • Development of a new algorithm using stochastic expectation maximization (SEM).
  • Comparative analysis with stochastic approximation expectation maximization (SAEM) and Monte Carlo Markov chain (MCMC) algorithms.
  • Application and validation using simulated psychological data and real-world datasets.

Main Results:

  • The proposed SEM algorithm demonstrated superior performance compared to SAEM and MCMC.
  • The study highlighted the critical impact of including maximum effects on model accuracy.
  • The algorithm effectively managed missing data in complex LMEM scenarios.

Conclusions:

  • The novel SEM algorithm is a highly preferable method for handling missing data in LMEM with multiple interactions.
  • The findings underscore the importance of robust algorithms for accurate statistical inference.
  • The proposed method offers a valuable tool for researchers dealing with incomplete datasets in LMEM.