Variable selection for the multivariate correlated response vector generalized linear model based on MCMC methods

Xinru Chen; Yu Fei; Songrui Bei; Feiyan Wang; Chengjin Tao

doi:10.1117/12.3025849

8 April 2024 Variable selection for the multivariate correlated response vector generalized linear model based on MCMC methods

Xinru Chen, Yu Fei, Songrui Bei, Feiyan Wang, Chengjin Tao

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901B (2024) https://doi.org/10.1117/12.3025849
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

The Multivariate Correlated Response Vector Generalized Linear Model is an important yet structurally complex class of generalized linear models. Commonly used variable selection methods, such as LASSO (Least Absolute Shrinkage and Selection Operator), are ineffective in efficiently identifying the crucial variables for this type of model. In this paper, we propose a novel variable selection method based on Markov chain Monte Carlo (MCMC) methods, specifically the Random Search method, to address this issue. This approach proves to be an effective variable selection method, as demonstrated by simulation analyses indicating its superior efficiency compared to LASSO and exhaustive subset search methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinru Chen, Yu Fei, Songrui Bei, Feiyan Wang, and Chengjin Tao "Variable selection for the multivariate correlated response vector generalized linear model based on MCMC methods", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901B (8 April 2024); https://doi.org/10.1117/12.3025849

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KEYWORDS

Feature selection

Statistical modeling

Heart

Cardiovascular disorders

Data modeling

Blood pressure

Binary data

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