用于无模型变量选择的Knockoff Boosted树
Knockoff Boosted Tree for Model-Free Variable Selection
摘要：
在这篇文章中，我们提出了一种新的策略进行变量选择没有事先模型拓扑知识，使用敲除方法与提升树模型。我们的方法的灵感来自原始的敲除方法，其中原始和敲除变量之间的差异用于变量选择与错误发现率控制。原始方法使用Lasso进行回归模型，并假设样本比变量多。我们将此方法扩展到无模型和高维变量选择。我们提出了两种新的抽样方法产生的仿冒品，即稀疏协方差和主成分仿冒方法。我们测试这些方法，并比较他们与原来的敲除方法的能力，以控制第一类错误和权力。提升树模型是一个复杂的系统，比具有简单假设的模型具有更多的超参数。在我们的框架中，这些超参数要么通过贝叶斯优化进行调整，要么固定在多个水平上进行趋势检测。在模拟试验中，我们还比较了树模型的重要性检验统计量的性质和性能。结果包括不同仿制品和重要性检验统计量的组合。我们还考虑了包括主效应、交互作用、指数和二阶模型的场景，同时假设真实的模型结构是未知的。我们将我们的算法应用于肿瘤纯度估计和肿瘤分类，使用癌症基因组图谱（TCGA）基因表达数据。所提出的算法包含在KOBT软件包中，可在\url{https://cran.r-project.org/web/packages/KOBT/index.html}获得。
英文摘要：
In this article, we propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. Our method is inspired by the original knockoff method, where the differences between original and knockoff variables are used for variable selection with false discovery rate control. The original method uses Lasso for regression models and assumes there are more samples than variables. We extend this method to both model-free and high-dimensional variable selection. We propose two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test these methods and compare them with the original knockoff method in terms of their ability to control type I errors and power. The boosted tree model is a complex system and has more hyperparameters than models with simpler assumptions. In our framework, these hyperparameters are either tuned through Bayesian optimization or fixed at multiple levels for trend detection. In simulation tests, we also compare the properties and performance of importance test statistics of tree models. The results include combinations of different knockoffs and importance test statistics. We also consider scenarios that include main-effect, interaction, exponential, and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using the Cancer Genome Atlas (TCGA) gene expression data. The proposed algorithm is included in the KOBT package, available at \url{https://cran.r-project.org/web/packages/KOBT/index.html}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
