Estimation and inference on central mean subspace for multivariate response data
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Publication:1663147
DOI10.1016/J.CSDA.2015.05.006zbMath1468.62243OpenAlexW2221099375MaRDI QIDQ1663147
Publication date: 21 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.2015.05.006
semiparametric efficiencysufficient dimension reductionprofile least squarescentral mean subspacemultivariate response
Related Items (4)
A selective review of sufficient dimension reduction for multivariate response regression ⋮ Nonlinear interaction detection through partial dimension reduction with missing response data ⋮ Dimension reduction estimation for central mean subspace with missing multivariate response ⋮ Minimum average variance estimation with group Lasso for the multivariate response central mean subspace
Cites Work
- Semiparametric least squares (SLS) and weighted SLS estimation of single-index models
- Regression analysis under link violation
- Dimension reduction for conditional mean in regression
- Optimal smoothing in single-index models
- Approximation Theorems of Mathematical Statistics
- 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
- On the Interpretation of Regression Plots
- Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
- An Adaptive Estimation of Dimension Reduction Space
- Semiparametric Estimation of Index Coefficients
- 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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