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Journal of Multivariate Analysis, 2021
This paper proposes a weighted version of the Minimum Average Variance Estimation (MAVE) method to estimate the Central Mean Subspace with multivariate response. The algorithm to implement the weighted MAVE method is provided. Asymptotic distribution of the MAVE estimator under the multivariate response setting is also derived.
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This paper proposes a weighted version of the Minimum Average Variance Estimation (MAVE) method to estimate the Central Mean Subspace with multivariate response. The algorithm to implement the weighted MAVE method is provided. Asymptotic distribution of the MAVE estimator under the multivariate response setting is also derived.
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Algebras with a Diagonable Subspace whose Centralizer Satisfies a Polynomial Identity
Canadian Journal of Mathematics, 1977The literature concerning rings with polynomial identity contains several theorems in which the existence of a polynomial identity on a subring implies the existence of such an identity on the ring itself. Belluce and Jain showed in 1968 that a prime ring will satisfy a polynomial identity provided it contains a right ideal with zero left annihilator ...
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A Shrinkage Estimation of Central Subspace in Sufficient Dimension Reduction
Communications in Statistics - Simulation and Computation, 2010Sliced regression is an effective dimension reduction method by replacing the original high-dimensional predictors with its appropriate low-dimensional projection. It is free from any probabilistic assumption and can exhaustively estimate the central subspace.
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Using intraslice covariances for improved estimation of the central subspace in regression
Biometrika, 2006SUMMARY Popular methods for estimating the central subspace in regression require slicing a continuous response. However, slicing can result in loss of information and in some cases that loss can be substantial. We use intraslice covariances to construct improved inference methods for the central subspace.
Cook, R. Dennis, Ni, Liqiang
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CSR: A Centrality Based on Robustness and Controllable Subspace of Complex Networks
2019 Chinese Control And Decision Conference (CCDC), 2019Complex networks are everywhere, some of them are airline network, road network, power grid network and protein-to-protein network. These networks are robust in failure or under attack but still needs in-depth study. Researchers have proposed various techniques to find and enhance the robustness of networks.
A. Mahmood, Umair Usman, Lin Wang
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Brain and other central nervous system tumor statistics, 2021
Ca-A Cancer Journal for Clinicians, 2021Kimberly D Miller +2 more
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Toxoplasma gondii infection and its implications within the central nervous system
Nature Reviews Microbiology, 2021Sumit Kumar +2 more
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Drug delivery to the central nervous system
Nature Reviews Materials, 2021Elizabeth Nance, Suzie H Pun
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