论文标题
惩罚回归具有多种先验效果的来源
Penalised regression with multiple sources of prior effects
论文作者
论文摘要
在许多高维预测或分类任务中，有关功能的互补数据可用，例如先前关于（EPI）遗传标记的生物学知识。在这里，我们考虑具有数值先验信息的任务，这些任务可洞悉特征效应的重要性（重量）和方向（符号），例如先前研究的回归系数。我们提出了一种将此类先前信息的多个来源集成到惩罚回归中的方法。如果有合适的二氧化碳，则可以改善预测性能，如仿真和应用所示。提出的方法在R软件包“ TransReg”（https://github.com/lcsb-bds/transreg）中实现。
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).
