  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.

Methodology;Machine Learning