Penalised regression with multiple sources of prior effects

2022/12/09 by Rauschenberger, Armin, Landoulsi, Zied, van de Wiel, Mark +1

paper · doi:10.17881/hczj-3297

Abstract

In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature eects, e.g. regression coefficients from previous studies. We propose an approach for integrating multiple sources of such prior information into penalised regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application. The proposed method is implemented in the R package transreg.

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