Influence Diagnostics for High-Dimensional Lasso Regression
From MaRDI portal
Publication:3391208
DOI10.1080/10618600.2019.1598869OpenAlexW2922636440WikidataQ128137187 ScholiaQ128137187MaRDI QIDQ3391208
Bala Rajaratnam, Doug Sparks, Honglin Yu, Steven Roberts
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2019.1598869
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Random lasso
- Least angle and \(\ell _{1}\) penalized regression: a review
- Stabilizing the Lasso against cross-validation variability
- Least angle regression. (With discussion)
- Robust regression through the Huber's criterion and adaptive lasso penalty
- High-dimensional influence measure
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Outlier Detection Using Nonconvex Penalized Regression
- Extended Bayesian information criteria for model selection with large model spaces
- Two graphical displays for outlying and influential observations in regression
- Detection of Influential Observation in Linear Regression
- Influential Observations in Linear Regression
- Regression Shrinkage and Selection via The Lasso: A Retrospective
- A Statistical View of Some Chemometrics Regression Tools
- Model selection procedure for high‐dimensional data
- Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data
- Asymptotic Analysis of Robust LASSOs in the Presence of Noise With Large Variance
- Outlier detection in high-dimensional regression model
- Linear mixed models for longitudinal data
This page was built for publication: Influence Diagnostics for High-Dimensional Lasso Regression