Multivariate regression modeling in integrative analysis via sparse regularization

Abstract

The multivariate regression model 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 extract useful information from multiple independent datasets, and then 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 group selection. Based on the idea of the alternating direction method of multipliers, we develop its computational algorithm. The performance of the proposed method is demonstrated through Monte Carlo simulation and analyzing wastewater treatment data with microbe measurements.