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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Variable selection in canonical discriminant analysis for family studies.

Man Jin1, Yixin Fang

  • 1Statistics and Evaluation Center, American Cancer Society, Atlanta, Georgia 30303, USA.

Biometrics
|April 9, 2010
PubMed
Summary

Canonical discriminant analysis (CDA) can overfit with many phenotypes. New bias correction and cross-validation methods estimate CDA ratios and aid variable selection in family studies.

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Area of Science:

  • Statistics
  • Genetics
  • Family Studies

Background:

  • Canonical discriminant analysis (CDA) is used in family studies to identify linear combinations of phenotypes.
  • High numbers of phenotypes in CDA can lead to overfitting, compromising results.
  • Accurate estimation of variability ratios is crucial for reliable analysis.

Purpose of the Study:

  • To develop methods for estimating predicted ratios in CDA.
  • To address overfitting issues in CDA when analyzing numerous phenotypes.
  • To enable variable selection within the CDA framework for family studies.

Main Methods:

  • Developed bias correction and cross-validation methods for ratio estimation in CDA.
  • Created an approximation to cross-validation to reduce computational intensity.
  • Applied these methods to perform variable selection in CDA.

Main Results:

  • The proposed methods effectively estimate predicted ratios associated with CDA coefficients.
  • Bias correction and cross-validation approaches provide reliable estimates.
  • The developed techniques facilitate variable selection, improving CDA model interpretability.

Conclusions:

  • New bias correction and cross-validation methods enhance the reliability of CDA in family studies.
  • These methods mitigate overfitting and improve the accuracy of variability ratio estimation.
  • The approach offers a robust strategy for variable selection in complex genetic analyses.