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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Model selection procedure for high-dimensional data.

Yongli Zhang1, Xiaotong Shen

  • 1Lundquist College of Business, University of Oregon, 1208 University Ave, Eugene, OR 97403 ( yongli@uoregon.edu ).

Statistical Analysis and Data Mining
|December 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces RIC(c), a novel criterion for variable selection in high-dimensional regression, ensuring accurate model identification. It combines with Least Angle Regression for efficient and reliable results, improving power market price forecasting.

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Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • High-dimensional regression presents challenges where predictors exceed sample size, necessitating variable selection.
  • Conventional criteria like BIC struggle with nonadaptivity and computational infeasibility in large model spaces.
  • Consistent model selection is crucial for reliable results in such scenarios.

Purpose of the Study:

  • To develop an adaptive model selection criterion for high-dimensional regression.
  • To propose a computationally feasible method for identifying the smallest true model.
  • To improve the accuracy of variable selection in complex datasets.

Main Methods:

  • Established a probability lower bound for selecting the smallest true model using an information criterion.
  • Proposed the RIC(c) (Regression Information Criterion with constant c) criterion, designed for model space adaptivity.
  • Developed a hybrid method combining Least Angle Regression (LAR) with RIC(c) for computational efficiency.

Main Results:

  • The proposed RIC(c) criterion adapts to the model space, overcoming limitations of conventional methods.
  • The combined LAR and RIC(c) method demonstrates a probability converging to one for identifying the smallest true model.
  • Empirical application in power market data showed superior price forecasting accuracy compared to backward variable selection.

Conclusions:

  • The RIC(c) criterion and its integration with LAR offer a robust solution for variable selection in high-dimensional regression.
  • This approach enhances model interpretability and predictive performance, particularly in fields like energy economics.
  • The method provides a computationally efficient and statistically sound alternative for complex modeling tasks.