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Variable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods.

L A Stefanski1, Yichao Wu1, Kyle White1

  • 1North Carolina State University.

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Summary
This summary is machine-generated.

A novel measurement-error-model approach enhances variable selection in machine learning. This method offers LASSO-like shrinkage for improved nonparametric classification accuracy.

Keywords:
AttenuationBayes ruleBinary regressionConvolutionDiscriminant analysisKernel discriminant analysisLASSOLinear regressionMaximum likelihood ruleModel selectionRidge regression

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Ridge regression and LASSO (Least Absolute Shrinkage and Selection Operator) estimation are established techniques.
  • Measurement error can attenuate relationships and impact model performance.
  • Existing variable selection methods may not adequately address measurement error.

Purpose of the Study:

  • To develop a new variable selection approach grounded in measurement error models.
  • To extend this approach to nonparametric classification.
  • To create a kernel-based classifier with shrinkage and variable selection capabilities.

Main Methods:

  • Developed a measurement-error-model-based variable selection technique.
  • Applied the approach to linear regression contexts.
  • Adapted the method for variable selection in nonparametric classification using a kernel-based approach.

Main Results:

  • Introduced a novel kernel-based classifier exhibiting LASSO-like shrinkage.
  • Demonstrated the classifier's variable selection properties.
  • Validated finite-sample performance through simulations and real data analysis.

Conclusions:

  • The proposed method effectively performs variable selection in nonparametric classification.
  • The approach incorporates measurement error considerations for potentially more robust results.
  • Theoretical consistency of the method was established.