面向复杂数据结构特征选择的非参数贝叶斯Knockoff生成器
Nonparametric Bayesian Knockoff Generators for Feature Selection Under Complex Data Structure
AI总结：
本文提出非参数贝叶斯GDPM模型生成高质量knockoff副本，用于复杂数据分布下的FDR控制特征选择，在模拟与TCGA卵巢癌数据中表现出准确FDR控制与更高功效。
摘要：
高维数据（如电子健康记录和遗传学数据）的近期激增为发现结局的新型预测因子提供了新机遇。面对大量候选特征，研究兴趣通常在于选择最有可能预测结局的特征以供进一步研究。在评估这些变量时，通常期望将错误发现率（FDR）控制在指定水平。Knockoff过滤是一种进行FDR控制特征选择的创新策略。本文提出了一种非参数贝叶斯模型，用于生成高质量knockoff副本，可提高对源自复杂分布（可能偏斜、高度分散和/或混合分布）的变量进行预测性特征识别的准确性。本文详细描述了通过MCMC后验抽样从GDPM模型生成knockoff副本的过程。此外，我们提供了关于knockoff过程鲁棒性的理论保证。通过模拟，表明该方法能以准确的FDR控制识别重要特征，且相比流行的二阶高斯knockoff生成器具有更高的功效。进一步，模型在FDR和功效方面与有限高斯混合knockoff生成器进行了比较。所提技术应用于利用The Cancer Genome Atlas (TCGA)数据检测预测卵巢癌患者生存的基因。
英文摘要：
The recent proliferation of high-dimensional data, such as electronic health records and genetics data, offers new opportunities to find novel predictors of outcomes. Presented with a large set of candidate features, interest often lies in selecting the ones most likely to be predictive of an outcome for further study. Controlling the false discovery rate (FDR) at a specified level is often desired in evaluating these variables. Knockoff filtering is an innovative strategy for conducting FDR-controlled feature selection. This paper proposes a nonparametric Bayesian model for generating high-quality knockoff copies that can improve the accuracy of predictive feature identification for variables arising from complex distributions, which can be skewed, highly dispersed and/or a mixture of distributions. This paper provides a detailed description for generating knockoff copies from a GDPM model via MCMC posterior sampling. Additionally, we provide a theoretical guarantee on the robustness of the knockoff procedure. Through simulations, the method is shown to identify important features with accurate FDR control and improved power over the popular second-order Gaussian knockoff generator. Furthermore, the model is compared with finite Gaussian mixture knockoff generator in FDR and power. The proposed technique is applied for detecting genes predictive of survival in ovarian cancer patients using data from The Cancer Genome Atlas (TCGA).
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