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Selective Inference for Group-Sparse Linear Models

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Fan Yang, Rina Foygel Barber, Prateek Jain, John Lafferty

Methodology · Submitted 27 Jul 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our main technical result gives the precise distribution of the magnitude of the projection of the data onto a given subspace, and enables us to develop inference procedures for a broad class of group-sparse selection methods, including the group lasso, iterative hard thresholding, and forward stepwise regression.

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arXiv ID1607.08211 (version 1)
Pith ID6BMRIAOS, Pith's permanent record of this paper and its review
CitationFan Yang, John Lafferty, Prateek Jain, Rina Foygel Barber. "Selective Inference for Group-Sparse Linear Models." arXiv:1607.08211 (2016). https://pith.science/paper/6BMRIAOS
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,973 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.56 · controlled comparison 0.03 · randomized assignment 0.02 · states quantitative result 0.24 · compares with prior results 0.09 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We develop tools for selective inference in the setting of group sparsity, including the construction of confidence intervals and p-values for testing selected groups of variables. Our main technical result gives the precise distribution of the magnitude of the projection of the data onto a given subspace, and enables us to develop inference procedures for a broad class of group-sparse selection methods, including the group lasso, iterative hard thresholding, and forward stepwise regression. We give numerical results to illustrate these tools on simulated data and on health record data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We develop tools for selective inference in the setting of group sparsity, including the construction of confidence intervals and p-values for testing selected groups of variables. contribution 1.00 0.95 0.18 asserted 1.00
s1 Our main technical result gives the precise distribution of the magnitude of the projection of the data onto a given subspace, and enables us to develop inference procedures for a broad class of group-sparse selection methods, including the group lasso, iterative hard thresholding, and forward stepwise regression. result 0.52 0.95 0.82 asserted 1.00
s2 We give numerical results to illustrate these tools on simulated data and on health record data. contribution 0.50 0.95 0.06 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.87 · evidence from proof or derivation 0.91.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We develop tools for selective inference in the setting of group sparsity, including the construction of confidence intervals and p-values for testing selected groups of variables. Our main technical result gives the precise distribution of the magnitude of the projection of the data onto a given subspace, and enables us to develop inference procedures for a broad class of group-sparse selection methods, including the group lasso, iterative hard thresholding, and forward stepwise regression. We give numerical results to illustrate these tools on simulated data and on health record data.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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