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

Shih-Ting Huang, Fang Xie and Johannes Lederer

Computational Statistics & Data Analysis, 2021, vol. 159, issue C

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.

Keywords: Generalized linear models; High-dimensional estimation; Ridge estimator (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:159:y:2021:i:c:s0167947321000396

DOI: 10.1016/j.csda.2021.107205

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