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A User-Friendly Computational Framework for Robust Structured Regression with the L2 Criterion.

Jocelyn T Chi1, Eric C Chi2

  • 1UCLA, Mathematics, Los Angeles, 90095 United States.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|February 1, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a flexible computational framework for robust structured regression using the L2 criterion. It simplifies complex procedures and identifies distinct data subpopulations effectively.

Keywords:
block-relaxationconvex optimizationminimum distance estimationregularization

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Area of Science:

  • Computational statistics
  • Machine learning
  • Data analysis

Background:

  • Structured regression methods are essential for analyzing complex data.
  • Existing methods often lack robustness or require intricate parameter tuning.
  • Identifying subpopulations within data is crucial for nuanced analysis.

Purpose of the Study:

  • To introduce a user-friendly computational framework for robust structured regression.
  • To enable robust regression with the L2 criterion under various structural constraints.
  • To facilitate the identification of heterogeneous subpopulations.

Main Methods:

  • Development of a novel computational framework for L2E regression.
  • Integration of algorithms for robust regression with structural constraints.
  • Incorporation of existing non-robust structured regression solvers.
  • Provision of convergence guarantees for the proposed framework.

Main Results:

  • The framework offers a user-friendly implementation of robust structured regression.
  • It effectively handles structural constraints without complex tuning.
  • The method successfully identifies heterogeneous subpopulations in data.
  • Demonstrated flexibility and performance through various examples.

Conclusions:

  • The introduced framework provides a robust and accessible tool for structured regression analysis.
  • It simplifies the implementation of advanced regression techniques.
  • The framework is valuable for identifying complex data structures and subpopulations.