FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing
FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing
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DOI：
10.32614/rj-2021-023
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
K. Bose;Jianqing Fan;Y. Ke;Xiaoou Pan;Wen-Xin Zhou
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文献类型：
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作者：
K. Bose;Jianqing Fan;Y. Ke;Xiaoou Pan;Wen-Xin Zhou
我们在R编程系统中提供了一个公开可用的库FarmTest。该库实现了Fan等人（2019）提出的因子调整稳健多重测试原理，用于对平均效应进行大规模同时推断。我们使用多因素模型来明确捕获大量变量之间的依赖关系。考虑了三种类型的因素：可观察因素、潜在因素和可观察因素和潜在因素的混合。本文还包括了非因素情况，即弱相关条件下的标准多重均值检验。该库实现了一系列自适应Huber方法与快速数据驱动调优方案相结合，以估计模型参数并构建对重尾和非对称误差分布具有鲁棒性的测试统计。对两样本多重均值检验问题的扩展也进行了讨论。给出了一些仿真实验和实际数据分析的结果。
We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed by Fan et al. (2019) for large-scale simultaneous inference on mean effects. We use a multi-factor model to explicitly capture the dependence among a large pool of variables. Three types of factors are considered: observable, latent, and a mixture of observable and latent factors. The non-factor case, which corresponds to standard multiple mean testing under weak dependence, is also included. The library implements a series of adaptive Huber methods integrated with fast data-driven tuning schemes to estimate model parameters and to construct test statistics that are robust against heavy-tailed and asymmetric error distributions. Extensions to two-sample multiple mean testing problems are also discussed. The results of some simulation experiments and a real data analysis are reported.