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Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives.
Multiple comparisons among means
Dunn, O. J. (1961) · 1961
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A simple sequentially rejective multiple test procedure
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Classes of orderings of measures and related correlation inequalities. i. multivariate totally positive distributions
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We need both exploratory and confirmatory
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A leisurely look at the bootstrap, the jackknife, and cross-validation
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A sharper bonferroni procedure for multiple tests of significance
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Bagging predictors
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Using adaptive bagging to debias regressions
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The control of the false discovery rate in multiple testing under dependency
Benjamini, Y. and Yekutieli, D. (2001) · 2001
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Classification of human lung carcinomas by mrna expression profiling reveals distinct adenocarcinoma subclasses
Bhattacharjee, A., Richards, W. G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., et al. (2001) · 2001
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Random forests
Breiman, L. (2001) · 2001
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Knockoffs with side information
Ren, Z. and Candès, E. (2020) · 2001
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Analyzing bagging
Bühlmann, P., Yu, B., et al. (2002) · 2002
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Familywise error rate control by interactive unmasking
Duan, B., Ramdas, A., and Wasserman, L. (2020) · 2002
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Cluster ensembles—a knowledge reuse framework for combining multiple partitions
Strehl, A. and Ghosh, J. (2002) · 2002
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Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data
Monti, S., Tamayo, P., Mesirov, J., and Golub, T. (2003) · 2003
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Multiple testing procedures with applications to genomics
Dudoit, S. and Van Der Laan, M. J. (2007) · 2007
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Sure independence screening for ultrahigh dimensional feature space
Fan, J. and Lv, J. (2008) · 2008
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A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11. 2 and 14q24. 1 (rad51l1)
Thomas, G., Jacobs, K. B., Kraft, P., Yeager, M., Wacholder, S., Cox, D. G., Hankinson, S. E., Hutchinson, A., Wang, Z., Yu, K., et al. (2009) · 2009
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High dimensional variable selection
Wasserman, L. and Roeder, K. (2009) · 2009
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Evaluation of association of hnf1b variants with diverse cancers: collaborative analysis of data from 19 genome-wide association studies
Elliott, K. S., Zeggini, E., McCarthy, M. I., Gudmundsson, J., Sulem, P., Stacey, S. N., Thorlacius, S., Amundadottir, L., Grönberg, H., Xu, J., et al. (2010) · 2010
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Stability selection
Meinshausen, N. and Bühlmann, P. (2010) · 2010
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Ensemble-based classifiers
Rokach, L. (2010) · 2010
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Balanced control of generalized error rates
Romano, J. P., Wolf, M., et al. (2010) · 2010
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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
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Multiple testing for exploratory research
Goeman, J. J., Solari, A., et al. (2011) · 2011
Large-scale association analysis in asians identifies new susceptibility loci for prostate cancer
Wang, M., Takahashi, A., Liu, F., Ye, D., Ding, Q., Qin, C., Yin, C., Zhang, Z., Matsuda, K., Kubo, M., et al. (2015) · 2015
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Cluster failure: Why fmri inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., and Knutsson, H. (2016) · 2016
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Familywise error rate control via knockoffs
Janson, L., Su, W., et al. (2016) · 2016
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Multiple testing of gene sets from gene ontology: possibilities and pitfalls
Meijer, R. J. and Goeman, J. J. (2016) · 2016
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Exact post-selection inference for sequential regression procedures
Tibshirani, R. J., Taylor, J., Lockhart, R., and Tibshirani, R. (2016) · 2016
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Controlling the family-wise error rate in multi-arm, multi-stage trials
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Cited alongside, same era.
Characterizing genetic risk at known prostate cancer susceptibility loci in african americans
Haiman, C. A., Chen, G. K., Blot, W. J., Strom, S. S., Berndt, S. I., Kittles, R. A., Rybicki, B. A., Isaacs, W. B., Ingles, S. A., Stanford, J. L., et al. (2011) · 2011
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Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study
Kote-Jarai, Z., Al Olama, A. A., Giles, G. G., Severi, G., Schleutker, J., Weischer, M., Campa, D., Riboli, E., Key, T., Gronberg, H., et al. (2011) · 2011
Cited alongside, same era.
Ensemble learning
Polikar, R. (2012) · 2012
Cited alongside, same era.
Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for alzheimer’s disease
Lambert, J.-C., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., Jun, G., DeStefano, A. L., Bis, J. C., Beecham, G. W., et al. (2013) · 2013
Cited alongside, same era.
Exact post-selection inference with the lasso
Lee, J. D., Sun, D. L., Sun, Y., and Taylor, J. E. (2013) · 2013
Cited alongside, same era.
Variable selection with error control: another look at stability selection
Shah, R. D. and Samworth, R. J. (2013) · 2013
Cited alongside, same era.
Crouch, L. A., Dodd, L. E., and Proschan, M. A. (2017) · 2017
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Semi-penalized inference with direct false discovery rate control for high-dimensional aft model
Ma, C. (2017) · 2017
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The uk biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · 2018
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Panning for gold:‘model-x’knockoffs for high dimensional controlled variable selection
Candès, E., Fan, Y., Janson, L., and Lv, J. (2018) · 2018
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Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in parkinson’s disease
Gao, C., Sun, H., Wang, T., Tang, M., Bohnen, N. I., Müller, M. L., Herman, T., Giladi, N., Kalinin, A., Spino, C., et al. (2018) · 2018
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Halving the bounds for the markov, chebyshev, and chernoff inequalities using smoothing
Huber, M. (2018) · 2018
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Ano7 is associated with aggressive prostate cancer
Kaikkonen, E., Rantapero, T., Zhang, Q., Taimen, P., Laitinen, V., Kallajoki, M., Jambulingam, D., Ettala, O., Knaapila, J., Boström, P. J., et al. (2018) · 2018
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Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci
Schumacher, F. R., Al Olama, A. A., Berndt, S. I., Benlloch, S., Ahmed, M., Saunders, E. J., Dadaev, T., Leongamornlert, D., Anokian, E., Cieza-Borrella, C., et al. (2018) · 2018
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Selective inference with unknown variance via the square-root lasso
Tian, X., Loftus, J. R., and Taylor, J. E. (2018) · 2018
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A knockoff filter for high-dimensional selective inference
Barber, R. F., Candès, E. J., et al. (2019) · 2019
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Gene hunting with hidden markov model knockoffs
Sesia, M., Sabatti, C., and Candès, E. J. (2019) · 2019
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Compositional knockoff filter for high-dimensional regression analysis of microbiome data
Srinivasan, A., Zhan, X., and Xue, L. (2019) · 2019
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The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
Candès, E. J. and Sur, P. (2020) · 2020
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Private communication
Spector, A. and Janson, L. (2020) · 2020
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Controlling the false discovery rate via knockoffs
Barber, R. F., Candès, E. J., et al. (2015) · 2085
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