Penalised regression with multiple sources of prior effects
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Abstract (arXiv)
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 effects, 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' (https://github.com/lcsb-bds/transreg).
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References · 5
- 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 69
- 2014Better prediction by use of co-data: Adaptive group-regularized ridge regressioncited by 18
- 2017High-Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transitioncited by 12
- 2020Feature-weighted elastic net: using "features of features" for better predictioncited by 6
- 2020Flexible co-data learning for high-dimensional predictioncited by 5
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