FarmTest-package: FarmTest: Factor-Adjusted Robust Multiple Testing

Description Details References


FarmTest package performs robust multiple testing for means in the presence of known and unknown latent factors (Fan et al, 2019). It implements a series of adaptive Huber methods combined with fast data-drive tuning schemes (Wang et al, 2020; Ke et al, 2019) to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or assymetric 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, covariance and regression estimators that are of independent interest.


See its GitHub page for details.


Ahn, S. C. and Horenstein, A. R. (2013). Eigenvalue ratio rest for the number of factors. Econometrica, 81(3) 1203–1227.

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. Stat. Methodol., 57 289–300.

Bose, K., Fan, J., Ke, Y., Pan, X. and Zhou, W.-X. (2019). FarmTest: An R package for factor-adjusted robust multiple testing, Preprint.

Fan, J., Ke, Y., Sun, Q. and Zhou, W-X. (2019). FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control. J. Amer. Statist. Assoc., 114, 1880-1893.

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Ke, Y., Minsker, S., Ren, Z., Sun, Q. and Zhou, W.-X. (2019). User-friendly covariance estimation for heavy-tailed distributions. Statis. Sci., 34, 454-471.

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Sun, Q., Zhou, W.-X. and Fan, J. (2020). Adaptive Huber regression. J. Amer. Statist. Assoc., 115, 254-265.

Wang, L., Zheng, C., Zhou, W. and Zhou, W.-X. (2020). A new principle for tuning-free Huber regression. Stat. Sin., to appear.

Zhou, W-X., Bose, K., Fan, J. and Liu, H. (2018). A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing. Ann. Statist., 46 1904-1931.

FarmTest documentation built on Sept. 7, 2020, 9:07 a.m.