The effect of data contamination in sliced inverse regression and finite sample breakdown point
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Publication:1744720
DOI10.1007/s13171-017-0102-xzbMath1387.62069OpenAlexW2617692546MaRDI QIDQ1744720
Publication date: 19 April 2018
Published in: Sankhyā. Series A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13171-017-0102-x
Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Nonparametric robustness (62G35)
Cites Work
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- Efficient estimation in sufficient dimension reduction
- Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
- High breakdown estimators for principal components: the projection-pursuit approach revis\-ited
- Coordinate-independent sparse sufficient dimension reduction and variable selection
- Dimension reduction for nonelliptically distributed predictors
- Student sliced inverse regression
- Robust inverse regression for dimension reduction
- A note On outlier sensitivity of Sliced Inverse Regression
- DETECTING INFLUENTIAL OBSERVATIONS IN SLICED INVERSE REGRESSION ANALYSIS
- Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
- Between-Groups Comparison of Principal Components
- Sliced Inverse Regression for Dimension Reduction
- Breakdown in Nonlinear Regression
- On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
- Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
- Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
- Identifying Regression Outliers and Mixtures Graphically
- Save: a method for dimension reduction and graphics in regression
- An Adaptive Estimation of Dimension Reduction Space
- Statistical Applications of a Metric on Subspaces to Satellite Meteorology
- A Semiparametric Approach to Dimension Reduction
- Groupwise Dimension Reduction
- Sequential Sufficient Dimension Reduction for Large p, Small n Problems
- Implications of influence function analysis for sliced inverse regression and sliced average variance estimation
- Sparse sufficient dimension reduction
- Influence Functions for Sliced Inverse Regression
- Using intraslice covariances for improved estimation of the central subspace in regression
- Sufficient Dimension Reduction via Inverse Regression
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