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Tuning-free ridge estimators for high-dimensional generalized linear models

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  • Huang, Shih-Ting
  • Xie, Fang
  • Lederer, Johannes

Abstract

Ridge estimators regularize the squared Euclidean lengths of parameters. Such estimators are mathematically and computationally attractive but involve tuning parameters that need to be calibrated. It is shown that ridge estimators can be modified such that tuning parameters can be avoided altogether, and the resulting estimator can improve on the prediction accuracies of standard ridge estimators combined with cross-validation.

Suggested Citation

  • Huang, Shih-Ting & Xie, Fang & Lederer, Johannes, 2021. "Tuning-free ridge estimators for high-dimensional generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:csdana:v:159:y:2021:i:c:s0167947321000396
    DOI: 10.1016/j.csda.2021.107205
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    References listed on IDEAS

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