Factor-Adjusted Robust Multiple Testing
Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <10.1080>.
Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <10.1214> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions.
Extensions to two-sample simultaneous mean comparison problems are also included.
As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.10.1214>10.1080>