Learn R Programming

FarmTest (version 2.0.1)

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

Description

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, 2018; 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 and covariance matrix estimators that are of independent interest.

Arguments

Details

For detailed information on how to use and install see its GitHub page https://github.com/XiaoouPan/FarmTest.

References

Ahn, S. C. and Horenstein, A. R. (2013). Eigenvalue ratio rest for the number of factors. Econometrica, 81(3) 1203<U+2013>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<U+2013>300.

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., to appear.

Huber, P. J. (1964). Robust estimation of a location parameter. Ann. Math. Statist., 35, 73<U+2013>101.

Ke, Y., Minsker, S., Ren, Z., Sun, Q. and Zhou, W.-X. (2019). User-friendly covariance estimation for heavy-tailed distributions: A survey and recent results. Statis. Sci., to appear.

Storey, J. D. (2002). A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B. Stat. Methodol., 64 479<U+2013>498.

Sun, Q., Zhou, W.-X. and Fan, J. (2019). Adaptive Huber regression. J. Amer. Statist. Assoc., to appear.

Wang, L., Zheng, C., Zhou, W. and Zhou, W.-X. (2018). A new principle for tuning-free Huber regression. Preprint.

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.