FarmTest: An R Package for Factor-Adjusted Robust Multiple Testing

Koushiki Bose, Jianqing Fan, Yuan Ke, Xiaoou Pan, Wen Xin Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed byFan 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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalR Journal
Volume12
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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