Dimension reduction estimation for central mean subspace with missing multivariate response

DOI10.1016/J.JMVA.2019.104542zbMath1428.62222OpenAlexW2970912230MaRDI QIDQ2008232

Han-Ying Liang, Guo-Liang Fan, Hong-Xia Xu

Publication date: 22 November 2019

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2019.104542

zbMATH Keywords

high dimensionalitymissing datasufficient dimension reductioncentral mean subspacemultivariate response

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20)

Related Items (2)

Nonlinear interaction detection through partial dimension reduction with missing response data ⋮ Advanced topics in sliced inverse regression

Cites Work

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• Dimension reduction estimation for probability density with data missing at random when covariables are present
• Goodness-of-fit tests for general linear models with covariates missed at random
• Weighted local linear CQR for varying-coefficient models with missing covariates
• Dimension reduction with missing response at random
• Estimation and inference on central mean subspace for multivariate response data
• Dimension reduction for conditional mean in regression
• Empirical likelihood for partially linear models
• Checking the adequacy of a general linear model with responses missing at random
• On relative efficiency of principal Hessian directions
• An estimating equation approach to dimension reduction for longitudinal data
• Semiparametric dimension reduction estimation for mean response with missing data
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Extending Sliced Inverse Regression
• An Adaptive Estimation of Dimension Reduction Space
• A Semiparametric Approach to Dimension Reduction
• Nonlinear interaction detection through model-based sufficient dimension reduction
• Sufficient Dimension Reduction under Dimension-reduction-based Imputation with Predictors Missing at Random
• Efficiency loss and the linearity condition in dimension reduction
• On Estimation Efficiency of the Central Mean Subspace
• On Sliced Inverse Regression With High-Dimensional Covariates

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