Discussion on: “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” by Dai, Lin, Zing, Liu
From MaRDI portal
Publication:6077545
DOI10.1080/01621459.2023.2223656OpenAlexW4385266930MaRDI QIDQ6077545
Maarten Jansen, Gerda Claeskens, Jing Zhou
Publication date: 18 October 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/01621459.2023.2223656
Cites Work
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Exact post-selection inference, with application to the Lasso
- The asymptotic distribution of the MLE in high-dimensional logistic models: arbitrary covariance
- The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square
- Generalized cross validation in variable selection with and without shrinkage
- A modern maximum-likelihood theory for high-dimensional logistic regression
- Information criteria for variable selection under sparsity
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models
This page was built for publication: Discussion on: “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” by Dai, Lin, Zing, Liu
