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Data-driven ridge regression for Aalen’s additive risk model

Author

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  • Boruvka, Audrey
  • Takahara, Glen
  • Tu, Dongsheng

Abstract

Two data-driven procedures, based respectively on the L-curve and generalized cross-validation, are proposed for ridge regression under Aalen’s additive risk model. Monte Carlo simulations show that the L-curve is a useful criterion for identifying a nominal degree of regularization that appreciably reduces variance, particularly in smaller samples.

Suggested Citation

  • Boruvka, Audrey & Takahara, Glen & Tu, Dongsheng, 2016. "Data-driven ridge regression for Aalen’s additive risk model," Statistics & Probability Letters, Elsevier, vol. 109(C), pages 189-193.
    • Handle: RePEc:eee:stapro:v:109:y:2016:i:c:p:189-193
    DOI: 10.1016/j.spl.2015.11.010
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    References listed on IDEAS

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    1. Odd O. Aalen & Johan Fosen & Harald Weedon-Fekjær & Ørnulf Borgan & Einar Husebye, 2004. "Dynamic Analysis of Multivariate Failure Time Data," Biometrics, The International Biometric Society, vol. 60(3), pages 764-773, September.
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