Exact post-selection inference with the lasso

By Jason D. Lee, Dennis L. Sun, Yuekai Sun, Jonathan E. Taylor. 2013

86 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/929f4c4fdf4df18911cb611ca9c43120de0cbffa

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Citations in the corpus

Uniform Convergence of Random Forests via Adaptive Concentration (2015, methodology): “Another approach to post-selection inference is to ﬁx the model-selection method and then explicitly condition on the selection event [18, 26, 29, 40].”
Uniform asymptotic inference and the bootstrap after model selection (2015, methodology): “the sequential regression setting, and is an example of a broader framework that we might call selective pivotal inference, applicable to many other settings, e.g., developed in Taylor et al. (2013), Lee et al. (2014), Lee & Taylor (2014), Loftus & Taylor (2014), Reid et al. (2014), Choi et ”
Familywise error rate control via knockoffs (2015, methodology): “(a) the generic step-down procedure of [27] applied to the least-squares pvalues (b) the step-up procedure of [26] applied to the least-squares p-values (c) the adaptation of Holm’s procedure to k-FWER applied to the leastsquares p-values [20] (d) for 1-FWER, the Lasso pathwise testing procedure of ”
Exact Post-Selection Inference for Sequential Regression Procedures (2014, background): “Lee et al. (2013), reporting on work concurrent with that of this paper, construct p-values and intervals for lasso coefficients at a fixed value of the regularization parameter λ (instead of a fixed number of steps k”
Sequential selection procedures and false discovery rate control (2013, methodology): “or asset, as the proliferation of methods for pathwise hypothesis testing suggests an interest in the topic (Lockhart et al., 2014; Loftus and Taylor, 2014; Fithian et al., 2014; G’Sell et al., 2013; Lee et al., 2013; Lee and Taylor, 2014; Taylor et al., 2013, 2014). Example. To further illustrate o”
Optimal Inference After Model Selection (2014, methodology): “In addition, we condition on the signs of the active lasso coeﬃcients, so that procedure Carve 100 is the inference-after-lasso test considered in Lee et al. (2013).”
A significance test for forward stepwise model selection (2014, methodology): “After selection, exact inference for a (randomly) chosen contrast η(y)Tµ of the mean vector µ could then be accomplished by analyzing the conditional distribution y ∼ N(µ, σ2I) ∣∣Ay ≤ b. (12) See Lee et al. (2013); Taylor et al. (2014) for further details on this approach.”
Selective Sequential Model Selection (2015, methodology): “(2014), Lee et al. (2013), and Loftus and Taylor (2014).”
Adaptive Concentration of Regression Trees, with Application to Random Forests (2015, background): “[26] Jason D Lee, Dennis L Sun, Yuekai Sun, and Jonathan E Taylor.”