On hybrid methods of inverse regression-based algorithms

DOI10.1016/j.csda.2006.01.005zbMath1161.62332OpenAlexW2095482618MaRDI QIDQ1019889

Megu Ohtaki, Yingxing Li, Li Xing Zhu

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.01.005

zbMATH Keywords

dimension reductionhybrid methodssliced average variance estimationsliced inverse regression

Mathematics Subject Classification ID

Multivariate analysis (62H99) Point estimation (62F10) Bayesian inference (62F15) Linear inference, regression (62J99)

Related Items (22)

Dimension reduction via adaptive slicing ⋮ A new sliced inverse regression method for multivariate response ⋮ On kernel method for sliced average variance estimation ⋮ A data-adaptive hybrid method for dimension reduction ⋮ The hybrid method of FSIR and FSAVE for functional effective dimension reduction ⋮ On a new hybrid estimator for the central mean space ⋮ SAVE: Robust or not? ⋮ General directional regression ⋮ Stationary subspace analysis based on second-order statistics ⋮ Iterative application of dimension reduction methods ⋮ An ensemble of inverse moment estimators for sufficient dimension reduction ⋮ Graph informed sliced inverse regression ⋮ Robust functional sliced inverse regression ⋮ Dimension Reduction in Regressions through Weighted Variance Estimation ⋮ A sliced inverse regression approach for data stream ⋮ Sliced average variance estimation for multivariate time series ⋮ Asymptotics for sliced average variance estimation ⋮ On splines approximation for sliced average variance estimation ⋮ A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches ⋮ Estimating multi-index models with response-conditional least squares ⋮ Dimension reduction based on weighted variance estimate ⋮ On central matrix based methods in dimension reduction

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