Dimension estimation in sufficient dimension reduction: a unifying approach

DOI10.1016/j.jmva.2010.08.007zbMath1206.62107OpenAlexW2092097721MaRDI QIDQ608333

Jiao Yang, Efstathia Bura

Publication date: 25 November 2010

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.08.007

zbMATH Keywords

random matrixSIRSAVEchi-square testsweighted chi-square tests

Mathematics Subject Classification ID

Related Items (20)

Distributed estimation in heterogeneous reduced rank regression: with application to order determination in sufficient dimension reduction ⋮ Supervised invariant coordinate selection ⋮ A robust proposal of estimation for the sufficient dimension reduction problem ⋮ Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models ⋮ Sufficient dimension reduction for clustered data via finite mixture modelling ⋮ Inference for the Dimension of a Regression Relationship Using Pseudo-Covariates ⋮ An improved sufficient dimension reduction-based kriging modeling method for high-dimensional evaluation-expensive problems ⋮ Using sliced inverse mean difference for sufficient dimension reduction ⋮ Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction ⋮ Sliced inverse median difference regression ⋮ Identification of structural vector autoregressions through higher unconditional moments ⋮ On principal graphical models with application to gene network ⋮ Graph informed sliced inverse regression ⋮ Interpretable sparse SIR for functional data ⋮ Using sliced mean variance-covariance inverse regression for classification and dimension reduction ⋮ Covariate Information Matrix for Sufficient Dimension Reduction ⋮ Bootstrap Testing of the Rank of a Matrix via Least-Squared Constrained Estimation ⋮ An Empirical Process View of Inverse Regression ⋮ Asymptotic and bootstrap tests for subspace dimension ⋮ On the usage of joint diagonalization in multivariate statistics

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