Journal of applied mathematics & informatics

한국전산응용수학회 (The Korean Society for Computational and Applied Mathematics)
권호 표지

ROBUST CROSS VALIDATIONS IN RIDGE REGRESSION

  • Jung, Kang-Mo (Department of Informatics and Statistics, Kunsan National University)
  • 발행 : 2009.05.31
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초록

The shrink parameter in ridge regression may be contaminated by outlying points. We propose robust cross validation scores in ridge regression instead of classical cross validation. We use robust location estimators such as median, least trimmed squares, absolute mean for robust cross validation scores. The robust scores have global robustness. Simulations are performed to show the effectiveness of the proposed estimators.

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참고문헌

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