# Multivariate regression modeling in integrative analysis via sparse regularization

arXiv: https://arxiv.org/abs/2304.07451 · HTML: https://mlsanity.com/paper/2304.07451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Online Proximal-ADMM For Time-varying Constrained Convex Optimization](https://mlsanity.com/paper/2005.03267/) (2020)
- [Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso](https://mlsanity.com/paper/1708.08911/) (2017)
- [Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization](https://mlsanity.com/paper/1501.03599/) (2015)
- [Dynamic Sharing Through the ADMM](https://mlsanity.com/paper/1702.03874/) (2017)
- [Integration of Survival Data from Multiple Studies](https://mlsanity.com/paper/2007.08594/) (2020)
- [Penalized integrative analysis under the accelerated failure time model](https://mlsanity.com/paper/1501.02458/) (2015)
- [Integrative Learning of Structured High-Dimensional Data from Multiple Datasets](https://mlsanity.com/paper/2207.00310/) (2022)
- [An integrative sparse boosting analysis of cancer genomic commonality and difference](https://mlsanity.com/paper/2211.17205/) (2022)

## Abstract (arXiv)

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.