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Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates in that model.
Semenova, L. and Rudin, C. (2019) · 1908
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A single-sample multiple-decision procedure for selecting the multinomial event which has the highest probability
Bechhofer, R. E., Elmaghraby, S., and Morse, N. (1959) · 1959
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A property of the multinomial distribution
Kesten, H. and Morse, N. (1959) · 1959
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On selecting the most probable category
Alam, K. (1971) · 1971
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On a subset selection procedure for the most probable event in a multinomial distribution
Panchapakesan, S. (1971) · 1971
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Inverse sampling subset selection of multinomial cells
Chen, P. (1986) · 1986
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A curtailed sequential procedure for subset selection of multinomial cells
Bechhoffer, R. E. and Chen, P. (1988) · 1988
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On selecting a subset containing the most probable multinomial event
Liu, H. C. and Lin, J. Y. (1991) · 1991
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Robust linear programming discrimination of two linearly inseparable sets
Bennett, K. P. and Mangasarian, O. L. (1992) · 1992
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Resampling fewer than n observations: Gains, losses, and remedies for losses
Bickel, P. J., Götze, F., and van Zwet, W. R. (1997) · 1997
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Cross-validation with confidence
Lei, J. (2020) · 1997
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Bootstrap Methods and Their Application
Davison, A. C. and Hinkley, D. V. (1999) · 1999
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Subsampling in the I.I.D. Case
Politis, D. N., Romano, J. P., and Wolf, M. (1999) · 1999
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · 2001
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Truncated inverse sampling procedure for multinomial subset selection
Chen, P. (1989) · 2002
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On selection and ranking procedures and order statistics from the multinomial distribution
Gupta, S. S. and Nagel, K. (1967) · 2002
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Least angle regression
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004) · 2004
Cited alongside, same era.
A note on a subset selection procedure for the most probable multinomial event
Panchapakesan, S. (2006) · 2006
Cited alongside, same era.
On model selection consistency of lasso
Zhao, P. and Yu, B. (2006) · 2006
Cited alongside, same era.
Controlling variable selection by the addition of pseudovariables
Wu, Y., Boos, D. D., and Stefanski, L. A. (2007) · 2007
Cited alongside, same era.
On combining machine learning with decision making
Tulabandhula, T. and Rudin, C. (2014) · 2014
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Confidence sets for model selection by f-testing
Ferrari, D. and Yang, Y. (2015) · 2015
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Familywise error rate control via knockoffs
Janson, L., Su, W., et al. (2016) · 2016
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Exact post-selection inference, with application to the lasso
Lee, J. D., Sun, D. L., Sun, Y., Taylor, J. E., et al. (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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Extended comparisons of best subset selection, forward stepwise selection, and the lasso
Hastie, T., Tibshirani, R., and Tibshirani, R. J. (2017) · 2017
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Bolasso: Model consistent lasso estimation through the bootstrap
Bach, F. R. (2008) · 2008
Cited alongside, same era.
Fence methods for mixed model selection
Jiang, J., Rao, J. S., Gu, Z., Nguyen, T., et al. (2008) · 2008
Cited alongside, same era.
On the consistency of feature selection using greedy least squares regression
Zhang, T. (2009) · 2009
Cited alongside, same era.
A selective overview of variable selection in high dimensional feature space
Fan, J. and Lv, J. (2010) · 2010
Cited alongside, same era.
Stability selection
Meinshausen, N. and Buhlmann, P. (2010) · 2010
Cited alongside, same era.
The model confidence set
Hansen, P. R., Lunde, A., and Nason, J. M. (2011) · 2011
Cited alongside, same era.
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Robust inference with knockoffs
Barber, R. F., Candès, E. J., and Samworth, R. J. (2018) · 2018
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Panning for gold: Model-x knockoffs for high dimensional controlled variable selection
Candes, E., Fan, Y., Janson, L., and Lv, J. (2018) · 2018
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Variable selection using pseudo-variables
Hu, W., Laber, E., and Stefanski, L. (2018) · 2018
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Model confidence bounds for variable selection
Li, Y., Luo, Y., Ferrari, D., Hu, X., and Qin, Y. (2018) · 2018
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Post-selection inference for-penalized likelihood models
Taylor, J. and Tibshirani, R. (2018) · 2018
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All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
Fisher, A., Rudin, C., and Dominici, F. (2019) · 2019
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Machine learning with operational costs
Tulabandhula, T. and Rudin, C. (2013) · 2028
Closest in time.
Controlling the false discovery rate via knockoffs
Barber, R. F. and Candes, E. J. (2015) · 2085
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