Ultrahigh-dimensional sufficient dimension reduction with measurement error in covariates

DOI10.1016/j.spl.2020.108931zbMath1456.62100OpenAlexW3089611765MaRDI QIDQ826677

Li-Pang Chen

Publication date: 6 January 2021

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2020.108931

zbMATH Keywords

measurement errorfeature selectiondistance correlationcumulative mean estimationultrahigh-dimension

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (1)

De-noising boosting methods for variable selection and estimation subject to error-prone variables

Cites Work

• On surrogate dimension reduction for measurement error regression: An invariance law
• Sliced Inverse Regression for Dimension Reduction
• Measurement Error Regression with Unknown Link: Dimension Reduction and Data Visualization
• Feature Screening via Distance Correlation Learning
• An Adaptive Estimation of Dimension Reduction Space
• Statistical analysis with measurement error or misclassification: Strategy, method and application. Grace Y. Yi. New York: Springer‐Verlag.
• Surrogate dimension reduction in measurement error regressions
• Dimension Reduction in Regressions Through Cumulative Slicing Estimation
• Sequential Sufficient Dimension Reduction for Large p, Small n Problems

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