Multivariate regression modeling in integrative analysis via sparse regularization

• URL: http://arxiv.org/abs/2304.07451v1
• Date: Sat, 15 Apr 2023 02:27:51 GMT
• Title: Multivariate regression modeling in integrative analysis via sparse regularization
• Authors: Shuichi Kawano, Toshikazu Fukushima, Junichi Nakagawa, Mamoru Oshiki
• Abstract summary: Integrative analysis is an effective method to pool useful information from multiple independent datasets. The integration is achieved by sparse estimation that performs variable and group selection. The performance of the proposed method is demonstrated through Monte Carlo simulation and analyzing wastewater treatment data with microbe measurements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multivariate regression model basically offers the analysis of a single dataset with multiple responses. However, such a single-dataset analysis often leads to unsatisfactory results. Integrative analysis is an effective method to pool useful information from multiple independent datasets and provides better performance than single-dataset analysis. In this study, we propose a multivariate regression modeling in integrative analysis. The integration is achieved by sparse estimation that performs variable and group selection. Based on the idea of alternating direction method of multipliers, we develop its computational algorithm that enjoys the convergence property. The performance of the proposed method is demonstrated through Monte Carlo simulation and analyzing wastewater treatment data with microbe measurements.

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