R package FarmTest: Factor Adjusted Robust Multiple Testing. Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a series of adaptive Huber methods combined with fast data-drive tuning schemes to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymetric error distributions. Extensions to two-sample simultaneous mean comparison problems are also included. As by-products, this package also contains functions that compute adaptive Huber mean and covariance matrix estimators that are of independent interest.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Chen, Yuxin; Chi, Yuejie; Fan, Jianqing; Ma, Cong; Yan, Yuling: Noisy matrix completion: understanding statistical guarantees for convex relaxation via nonconvex optimization (2020)
- Fan, Jianqing; Feng, Yang; Xia, Lucy: A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models (2020)
- Bailey, Natalia; Pesaran, M. Hashem; Smith, L. Vanessa: A multiple testing approach to the regularisation of large sample correlation matrices (2019)
- Fan, Jianqing; Ke, Yuan; Sun, Qiang; Zhou, Wen-Xin: Farmtest: factor-adjusted robust multiple testing with approximate false discovery control (2019)
- Yu, Long; He, Yong; Zhang, Xinsheng: Robust factor number specification for large-dimensional elliptical factor model (2019)