Grouped variable screening for ultra-high dimensional data for linear model
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Publication:2291335
DOI10.1016/j.csda.2019.106894zbMath1504.62102OpenAlexW2995199152MaRDI QIDQ2291335
Publication date: 30 January 2020
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2019.106894
Related Items (3)
The Kendall interaction filter for variable interaction screening in high dimensional classification problems ⋮ Grouped feature screening for ultra-high dimensional data for the classification model ⋮ Projection quantile correlation and its use in high-dimensional grouped variable screening
Cites Work
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- A group bridge approach for variable selection
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- The Sparse MLE for Ultrahigh-Dimensional Feature Screening
- Interaction Screening for Ultrahigh-Dimensional Data
- An iterative approach to distance correlation-based sure independence screening
- High Dimensional Ordinary Least Squares Projection for Screening Variables
- Model Selection and Estimation in Regression with Grouped Variables
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