A Model Selection Criterion for High-Dimensional Linear Regression
DOI10.1109/TSP.2018.2821628zbMATH Open1414.62310DBLPjournals/tsp/OwrangJ18OpenAlexW2795530222WikidataQ62585863 ScholiaQ62585863MaRDI QIDQ4622233
Publication date: 12 February 2019
Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1109/tsp.2018.2821628
Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03)
Related Items (4)
Recommendations
- Title not available (Why is that?) 👍 👎
- Title not available (Why is that?) 👍 👎
- Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis 👍 👎
- A systematic review on model selection in high-dimensional regression 👍 👎
- Model selection for high-dimensional linear regression with dependent observations 👍 👎
- Model selection procedure for high‐dimensional data 👍 👎
- A Model-Averaging Approach for High-Dimensional Regression 👍 👎
- High-dimensional linear model selection motivated by multiple testing 👍 👎
- A semi-parametric approach to feature selection in high-dimensional linear regression models 👍 👎
This page was built for publication: A Model Selection Criterion for High-Dimensional Linear Regression
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q4622233)