An Empirical Bayes Approach to Regularization Using Previously Published Models
Abstract
This manuscript proposes a novel empirical Bayes technique for regularizing regression coefficients in predictive models. When predictions from a previously published model are available, this empirical Bayes method provides a natural mathematical framework for shrinking coefficients toward the estimates implied by the body of existing research rather than the shrinkage toward zero provided by traditional L1 and L2 penalization schemes. The method is applied to two different prediction problems. The first involves the construction of a model for predicting whether a single nucleotide polymorphism (SNP) of the KCNQ1 gene will result in dysfunction of the corresponding voltage gated ion channel. The second involves the prediction of preoperative serum creatinine change in patients undergoing cardiac surgery.
Cite
@article{arxiv.1710.03866,
title = {An Empirical Bayes Approach to Regularization Using Previously Published Models},
author = {Derek K Smith and Loren E Smith and Brett Kroncke and Frederic T Billings and Jens Meiler and Jeffrey Blume},
journal= {arXiv preprint arXiv:1710.03866},
year = {2017}
}