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Related Experiment Video

Updated: May 25, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Adaptive CoCoLasso for High-Dimensional Measurement Error Models.

Qin Yu1

  • 1School of Management, University of Science and Technology of China, Hefei 230026, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Adaptive CoCoLasso for high-dimensional regression with noisy data. It balances prediction accuracy and feature selection, outperforming other methods in scenarios with measurement errors.

Keywords:
Adaptive CoCoLassohigh-dimensional regressionmeasurement errornearest positive semi-definite projection

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Most high-dimensional regression studies focus on clean data.
  • Real-world data often contain missing values and measurement errors.
  • Effective methods for contaminated high-dimensional data are underexplored.

Purpose of the Study:

  • Introduce Adaptive CoCoLasso for high-dimensional linear models with error-prone measurements.
  • Address the need for methods balancing prediction accuracy, feature selection, and computational efficiency.
  • Provide theoretical guarantees for the proposed estimator.

Main Methods:

  • Adaptive convex conditioned Lasso (Adaptive CoCoLasso) estimator.
  • Combines projection onto the nearest positive semi-definite matrix.
  • Utilizes an adaptively weighted ℓ1 penalty.

Main Results:

  • Adaptive CoCoLasso shows strong performance in prediction accuracy and mean squared error.
  • Effective in handling both additive and multiplicative measurement noise.
  • Offers a favorable trade-off between prediction accuracy and sparse modeling.

Conclusions:

  • Adaptive CoCoLasso is a robust approach for high-dimensional regression with contaminated covariates.
  • The method provides a good balance between predictive performance and model sparsity.
  • Demonstrates strong performance in synthetic data analysis with various noise types.