A Bayesian Approach for Robust Longitudinal Envelope Models
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Wiley Open Access
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| Format: | Artículo Open Access |
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Wiley
2026
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| _version_ | 1867021217361297408 |
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| author | Peng Zeng Yushan Mu |
| author_facet | Peng Zeng Yushan Mu Peng Zeng Yushan Mu |
| collection | Wiley Open Access |
| contents | A Bayesian Approach for Robust Longitudinal Envelope Models Peng Zeng Yushan Mu Statistics in Medicine ABSTRACT The envelope model provides a dimension‐reduction framework for multivariate linear regression. However, existing envelope methods typically assume normally distributed random errors and do not accommodate repeated measures in longitudinal studies. To address these limitations, we propose the robust longitudinal envelope model (RoLEM). RoLEM employs a scale mixture of matrix‐variate normal distributions to model random errors, allowing it to handle potential outliers, and incorporates flexible correlation structures for repeated measurements. In addition, we introduce new prior and proposal distributions on the Grassmann manifold to facilitate Bayesian inference for RoLEM. Simulation studies and real data analysis demonstrate the superior performance of the proposed method. 10.1002/sim.70496 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.70496 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70496 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | A Bayesian Approach for Robust Longitudinal Envelope Models Peng Zeng Yushan Mu Statistics in Medicine A Bayesian Approach for Robust Longitudinal Envelope Models Peng Zeng Yushan Mu Statistics in Medicine ABSTRACT The envelope model provides a dimension‐reduction framework for multivariate linear regression. However, existing envelope methods typically assume normally distributed random errors and do not accommodate repeated measures in longitudinal studies. To address these limitations, we propose the robust longitudinal envelope model (RoLEM). RoLEM employs a scale mixture of matrix‐variate normal distributions to model random errors, allowing it to handle potential outliers, and incorporates flexible correlation structures for repeated measurements. In addition, we introduce new prior and proposal distributions on the Grassmann manifold to facilitate Bayesian inference for RoLEM. Simulation studies and real data analysis demonstrate the superior performance of the proposed method. 10.1002/sim.70496 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | A Bayesian Approach for Robust Longitudinal Envelope Models |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70496 |