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<p>High-dimensional linear mixed models are widely used for longitudinal data analysis, yet their reliance on normality assumptions often limits applicability in psychometric and biomedical settings. To address this, the authors propose a high-dimensional skew-normal linear mixed model and develop a novel variational Baysian method that integrates spike-and-slab Lasso priors for simultaneous parameter estimation and variable selection. To handle dependencies in the joint posterior, the authors propose a variational auto-encoders to extract latent features, and employ a coordinate ascent algorithm to optimize the evidence lower bound (ELBO), circumventing intractable integrals. Model comparison is conducted using the Bayes factor, approximated via the ELBO. The effectiveness of the proposed methodologies is demonstrated through simulation studies and a real-data application.</p>

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Auto-Encoding Variational Bayesian Inference in High-Dimensional Skew-Normal Linear Mixed Models

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Jieyi Yi,
  • Niansheng Tang,
  • Ying Wu,
  • Tong Su

摘要

High-dimensional linear mixed models are widely used for longitudinal data analysis, yet their reliance on normality assumptions often limits applicability in psychometric and biomedical settings. To address this, the authors propose a high-dimensional skew-normal linear mixed model and develop a novel variational Baysian method that integrates spike-and-slab Lasso priors for simultaneous parameter estimation and variable selection. To handle dependencies in the joint posterior, the authors propose a variational auto-encoders to extract latent features, and employ a coordinate ascent algorithm to optimize the evidence lower bound (ELBO), circumventing intractable integrals. Model comparison is conducted using the Bayes factor, approximated via the ELBO. The effectiveness of the proposed methodologies is demonstrated through simulation studies and a real-data application.