mlsanityData to 14 Oct 2026

Selective inference using randomized group lasso estimators for general models

arXiv 2306.13829 ↗First posted 24 Jun 2023stat.ME math.ST stat.MLcited by 3

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Abstract (arXiv)

Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions. The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data, for example, and allows for categorical or grouped covariates as well as continuous covariates. A randomized group-regularized optimization problem is studied. The added randomization allows us to construct a post-selection likelihood which we show to be adequate for selective inference when conditioning on the event of the selection of the grouped covariates. This likelihood also provides a selective point estimator, accounting for the selection by the group lasso. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume. The selective inference method for grouped lasso is illustrated on data from the national health and nutrition examination survey while simulations showcase its behaviour and favorable comparison with other methods.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 24 Jun 2023.

Builds on

17 of 49 references.

References · 17

  1. 2018A modern maximum-likelihood theory for high-dimensional logistic regressioncited by 81
  2. 2014Optimal Inference After Model Selectioncited by 67
  3. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 25
  4. 2020Smoothed Quantile Regression with Large-Scale Inferencecited by 23
  5. 2021Splitting strategies for post-selection inferencecited by 16
  6. 2017The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Squarecited by 15
  7. 2021Data fission: splitting a single data pointcited by 14
  8. 2019Approximate selective inference via maximum likelihoodcited by 8
  9. 2020A Scale-free Approach for False Discovery Rate Control in Generalized Linear Modelscited by 8
  10. 2023Generalized Data Thinning Using Sufficient Statisticscited by 8
  11. 2022Exact Selective Inference with Randomizationcited by 6
  12. 2016Post-selection inference for L1-penalized likelihood modelscited by 5
5 more
  1. 2020Multicarving for high-dimensional post-selection inferencecited by 5
  2. 2020Approximate Post-Selective Inference for Regression with the Group LASSOcited by 4
  3. 2020Integrative Bayesian models using Post-selective Inference: a case study in Radiogenomicscited by 3
  4. 2019Constructing confidence sets after lasso selection by randomized estimator augmentationcited by 1
  5. 2023Selective Inference with Distributed Datacited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Flexible Selective Inference with Flow-based Transport Mapscited by 0
  2. 2025Hierarchical Clustering With Confidencecited by 0
  3. 2026Interpretable AI with Local Distillationcited by 0