FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing
Koushiki Bose, Jianqing Fan, Yuan Ke, Xiaoou Pan and Wen-Xin Zhou
, The R Journal (2020) 12:2, pages 372-387.
Abstract 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.
Received: 2020-06-03; online 2021-01-15, supplementary material, (429.6 KiB)@article{RJ-2021-023, author = {Koushiki Bose and Jianqing Fan and Yuan Ke and Xiaoou Pan and Wen-Xin Zhou}, title = {{FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing}}, year = {2021}, journal = {{The R Journal}}, doi = {10.32614/RJ-2021-023}, url = {https://doi.org/10.32614/RJ-2021-023}, pages = {372--387}, volume = {12}, number = {2} }