On surrogate dimension reduction for measurement error regression: An invariance law

DOI10.1214/009053607000000172zbMath1126.62055arXiv0712.0892OpenAlexW3098656207MaRDI QIDQ2466685

Xiangrong Yin, Bing Li

Publication date: 16 January 2008

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0712.0892

zbMATH Keywords

invarianceregression graphicscentral mean spacecentral spacessurrogate predictors and responseweak convergence in probability

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) General nonlinear regression (62J02) Functional limit theorems; invariance principles (60F17)

Related Items (10)

Ultrahigh-dimensional sufficient dimension reduction with measurement error in covariates ⋮ Dimension reduction based on conditional multiple index density function ⋮ Sufficient dimension reduction for survival data analysis with error-prone variables ⋮ Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates ⋮ Direction estimation in single-index models via distance covariance ⋮ Likelihood-based surrogate dimension reduction ⋮ On a dimension reduction regression with covariate adjustment ⋮ A Note on the Invariance Law for Surrogate Dimension Reduction ⋮ Pseudo estimation and variable selection in regression ⋮ Independence tests in the presence of measurement errors: an invariance law

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