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2021 Tuning parameter calibration for personalized prediction in medicine
Shih-Ting Huang, Yannick Düren, Kristoffer H. Hellton, Johannes Lederer
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Electron. J. Statist. 15(2): 5310-5332 (2021). DOI: 10.1214/21-EJS1884

Abstract

Personalized prediction is of high interest in medicine; potential applications include the prediction of individual drug responses or risks of complications. But typical statistical pipelines such as ridge estimation combined with cross-validation ignore the heterogeneity among the patients and, therefore, are not suited for personalized prediction. We, therefore, introduce an alternative ridge-type pipeline that can minimize the prediction error of each patient individually. We show that our pipeline is optimal in terms of oracle inequalities, fast, and highly effective both in simulations and on real data.

Funding Statement

JL and YD acknowledge partial financial support from the DFG project number 451920280.

Citation

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Shih-Ting Huang. Yannick Düren. Kristoffer H. Hellton. Johannes Lederer. "Tuning parameter calibration for personalized prediction in medicine." Electron. J. Statist. 15 (2) 5310 - 5332, 2021. https://doi.org/10.1214/21-EJS1884

Information

Received: 1 April 2021; Published: 2021
First available in Project Euclid: 15 December 2021

Digital Object Identifier: 10.1214/21-EJS1884

Keywords: Euclidean distance ridge , high-dimensional estimation , Personalized medicine , regularization , Ridge regression , tuning parameter calibration

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Vol.15 • No. 2 • 2021
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