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.

翻译：多元回归模型主要提供对具有多个响应的单一数据集的分析。然而，这种单一数据集的分析往往导致不令人满意的结果。整合分析是一种有效的方法，能够汇集来自多个独立数据集的有用信息，并提供比单一数据集分析更优的性能。在本研究中，我们提出一种整合分析中的多元回归建模方法。该整合通过执行变量和组选择的稀疏估计来实现。基于交替方向乘子法的思想，我们开发了具有收敛性质的相应计算算法。通过蒙特卡罗模拟以及分析包含微生物测量的废水处理数据，验证了所提方法的性能。