Fetching the paper…
Reading the bibliography…
In many scientific problems, researchers try to relate a response variable $Y$ to a set of potential explanatory variables $X = (X_1,\dots,X_p)$, and start by trying to identify variables that contribute to this relationship.
Nodewise knockoffs: False discovery rate control for gaussian graphical models
Li, J. and Maathuis, M. H. (2019) · 1908
Earlier work this paper cites.
The distribution of quadratic forms in a normal system, with applications to the analysis of covariance
Cochran, W. G. (1934) · 1934
Earlier work this paper cites.
The measure of the critical values of differentiable maps
Sard, A. (1942) · 1942
Earlier work this paper cites.
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Benjamini, Y. and Hochberg, Y. (1995) · 1995
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · 1996
Earlier work this paper cites.
Causal inference in genetic trio studies
Bates, S., Sesia, M., Sabatti, C., and Candès, E. (2020b) · 2002
Earlier work this paper cites.
Liang, T. and Sur, P. (2020) · 2002
Earlier work this paper cites.
On non-parametric testing, the uniform behaviour of the t-test, and related problems
Romano, J. P. (2004) · 2004
Earlier work this paper cites.
Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach
Storey, J. D., Taylor, J. E., and Siegmund, D. (2004) · 2004
Earlier work this paper cites.
A theoretical treatment of conditional independence testing under model-x
Katsevich, E. and Ramdas, A. (2020) · 2005
Earlier work this paper cites.
Interpretable signal analysis with knockoffs enhances classification of bacterial raman spectra
Chia, C., Sesia, M., Ho, C.-S., Jeffrey, S. S., Dionne, J., Candès, E. J., and Howe, R. T. (2020) · 2006
Earlier work this paper cites.
On the Benjamini–Hochberg method
Ferreira, J., Zwinderman, A., et al. (2006) · 2006
Earlier work this paper cites.
Testing statistical hypotheses
Lehmann, E. L. and Romano, J. P. (2006) · 2006
Earlier work this paper cites.
Fast and powerful conditional randomization testing via distillation
Liu, M., Katsevich, E., Janson, L., and Ramdas, A. (2020) · 2006
Cited alongside, same era.
The lasso with general Gaussian designs with applications to hypothesis testing
Celentano, M., Montanari, A., and Wei, Y. (2020) · 2007
Cited alongside, same era.
A power analysis for knockoffs with the lasso coefficient-difference statistic
Weinstein, A., Su, W. J., Bogdan, M., Barber, R. F., and Candès, E. J. (2020) · 2007
Cited alongside, same era.
Linear models in statistics
Rencher, A. C. and Schaalje, G. B. (2008) · 2008
Cited alongside, same era.
Screen and clean: a tool for identifying interactions in genome-wide association studies
Wu, J., Devlin, B., Ringquist, S., Trucco, M., and Roeder, K. (2010) · 2010
Adapt: an interactive procedure for multiple testing with side information
Lei, L. and Fithian, W. (2018) · 2018
Later among the works it cites.
DeepPINK: reproducible feature selection in deep neural networks
Lu, Y., Fan, Y., Lv, J., and Noble, W. S. (2018) · 2018
Later among the works it cites.
Gene hunting with hidden Markov model knockoffs
Sesia, M., Sabatti, C., and Candès, E. J. (2018) · 2018
Later among the works it cites.
The holdout randomization test: Principled and easy black box feature selection
Tansey, W., Veitch, V., Zhang, H., Rabadan, R., and Blei, D. M. (2018) · 2018
Later among the works it cites.
Significance testing in non-sparse high-dimensional linear models
Zhu, Y. and Bradic, J. (2018) · 2018
Later among the works it cites.
On the construction of knockoffs in case–control studies
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Waste not, want not: why rarefying microbiome data is inadmissible
McMurdie, P. J. and Holmes, S. (2014) · 2014
Cited alongside, same era.
The LASSO risk for Gaussian matrices
Bayati, M. and Montanari, A. (2011) · 2017
Cited alongside, same era.
A power and prediction analysis for knockoffs with lasso statistics
Weinstein, A., Barber, R., and Candes, E. (2017) · 2017
Cited alongside, same era.
Robust inference with knockoffs
Barber, R. F., Candès, E. J., and Samworth, R. J. (2018) · 2018
Cited alongside, same era.
Panning for gold: Model-X knockoffs for high-dimensional controlled variable selection
Candès, E., Fan, Y., Janson, L., and Lv, J. (2018) · 2018
Cited alongside, same era.
Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J. (2018) · 2018
Cited alongside, same era.
IPAD: stable interpretable forecasting with knockoffs inference
Fan, Y., Lv, J., Sharifvaghefi, M., and Uematsu, Y. (2018) · 2018
Cited alongside, same era.
Barber, R. F. and Candès, E. (2019) · 2019
Later among the works it cites.
The conditional permutation test for independence while controlling for confounders
Berrett, T. B., Wang, Y., Barber, R. F., and Samworth, R. J. (2019) · 2019
Later among the works it cites.
Multilayer knockoff filter: Controlled variable selection at multiple resolutions
Katsevich, E. and Sabatti, C. (2019) · 2019
Later among the works it cites.
Power analysis of knockoff filters for correlated designs
Liu, J. and Rigollet, P. (2019) · 2019
Later among the works it cites.
A modern maximum-likelihood theory for high-dimensional logistic regression
Sur, P. and Candès, E. J. (2019) · 2019
Later among the works it cites.
Relaxing the assumptions of knockoffs by conditioning
Huang, D. and Janson, L. (2020) · 2020
Closest in time.
Conditional resampling improves sensitivity and specificity of single cell crispr regulatory screens
Katsevich, E. and Roeder, K. (2020) · 2020
Closest in time.