Model selection with mixed variables on the Lasso path
From MaRDI portal
Publication:2040668
DOI10.1007/s13571-019-00219-5zbMath1469.62305OpenAlexW3022060988MaRDI QIDQ2040668
Huimin Peng, X. Jessie Jeng, Wen-Bin Lu
Publication date: 14 July 2021
Published in: Sankhyā. Series B (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13571-019-00219-5
Ridge regression; shrinkage estimators (Lasso) (62J07) Statistical ranking and selection procedures (62F07)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
- Exact post-selection inference, with application to the Lasso
- Asymptotic Bayes-optimality under sparsity of some multiple testing procedures
- Controlling the false discovery rate via knockoffs
- SLOPE-adaptive variable selection via convex optimization
- False discoveries occur early on the Lasso path
- Least angle regression. (With discussion)
- A significance test for the lasso
- Predictor ranking and false discovery proportion control in high-dimensional regression
- Variable Selection in High-Dimensional Multivariate Binary Data with Application to the Analysis of Microbial Community DNA Fingerprints
- Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures
- Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- Efficient Signal Inclusion With Genomic Applications
- Sequential Selection Procedures and False Discovery Rate Control
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models