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Multi-Platform Multivariate Regression with Group Sparsity for High-Dimensional Data Integration.

Shanshan Qin1, Guanlin Zhang2, Xin Gao2

  • 1School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China.

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Summary
This summary is machine-generated.

This study introduces a new model for high-dimensional regression with multiple outcomes across platforms. It effectively fuses cross-platform data and models within-platform correlations for better insights.

Keywords:
data integrationgroup sparsityhigh-dimensionalmulti-platformmultivariate regression

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Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • High-dimensional regression with multivariate responses presents challenges, especially with multi-platform data.
  • Correlated outcomes within and across platforms complicate modeling and analysis.

Purpose of the Study:

  • To introduce a novel multi-platform multivariate high-dimensional linear regression (MM-HLR) model.
  • To simultaneously model within-platform correlation and enable cross-platform information fusion.

Main Methods:

  • Utilized a mixture of Lasso and group Lasso penalties for predictor and group sparsity.
  • Developed an efficient algorithm using iteratively reweighted least squares and block coordinate descent.

Main Results:

  • Established theoretical guarantees including oracle bounds for prediction error, estimation accuracy, and support recovery.
  • Simulation studies demonstrated low bias, small variance, and robustness across dimensions.

Conclusions:

  • The MM-HLR model effectively integrates multivariate responses and multi-platform data.
  • Empirical results and financial data analysis confirm performance gains and enhanced estimation stability.