Robust estimation and variable selection in sufficient dimension reduction
From MaRDI portal
Publication:1658471
DOI10.1016/j.csda.2016.11.007zbMath1466.62182OpenAlexW2552739711MaRDI QIDQ1658471
Qin Wang, Hossein Moradi Rekabdarkolaee, Edward L. Boone
Publication date: 14 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2016.11.007
Related Items (5)
2nd special issue on robust analysis of complex data ⋮ Variable selection through adaptive MAVE ⋮ Dimension reduction in nonparametric models of production ⋮ High-dimensional sign-constrained feature selection and grouping ⋮ Robust sufficient dimension reduction via ball covariance
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sliced Regression for Dimension Reduction
- The Adaptive Lasso and Its Oracle Properties
- An adaptive estimation of MAVE
- Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
- Robust estimation of dimension reduction space
- A nonlinear multi-dimensional variable selection method for high dimensional data: sparse MAVE
- A journey in single steps: robust one-step \(M\)-estimation in linear regression
- Robust variable selection through MAVE
- Regression analysis under link violation
- Structure adaptive approach for dimension reduction.
- Dimension reduction for conditional mean in regression
- Variable bandwidth and one-step local \(M\)-estimator
- Contour regression: a general approach to dimension reduction
- Local modal regression
- Investigating Smooth Multiple Regression by the Method of Average Derivatives
- Sliced Inverse Regression for Dimension Reduction
- On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
- Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
- An Adaptive Estimation of Dimension Reduction Space
- Comment
This page was built for publication: Robust estimation and variable selection in sufficient dimension reduction