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Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

Ros T, Férat V, Huang Y, Colangelo C, Kia SM, Wolfers T, Vulliemoz S, Michela A.   +7 more
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Central Mean Subspace in Time Series

Journal of Computational and Graphical Statistics, 2009
We propose a notion of central mean dimension reduction subspace for time series {xt} which does not require specification of a model but seeks to find a p×d matrix Φd, d≤p, so that the d×1 vector ΦdTXt−1, where Xt−1=(xt−1, …, xt−p)T for some p≥1, includes all the information about xt that is available from E(xt|Xt−1).
Jin-Hong Park, T. N. Sriram, Xiangrong Yin   +2 more
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Estimation and inference on central mean subspace for multivariate response data

Computational Statistics & Data Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhu, Liping, Zhong, Wei
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Feature filter for estimating central mean subspace and its sparse solution

Computational Statistics & Data Analysis, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Pei, Yin, Xiangrong, Yuan, Qingcong, Kryscio, Richard   +3 more
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Fused clustering mean estimation of central subspace

Journal of the Korean Statistical Society, 2020
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Um, Hye Yeon, Yoo, Jae Keun
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Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace

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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Dimension reduction estimation for central mean subspace with missing multivariate response

Journal of Multivariate Analysis, 2019
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Fan, Guo-Liang, Xu, Hong-Xia, Liang, Han-Ying   +2 more
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Determining the dimension of the central subspace and central mean subspace

Biometrika, 2008
The central subspace and central mean subspace are two important targets of sufficient dimension reduction. We propose a weighted chi-squared test to determine their dimensions based on matrices whose column spaces are exactly equal to the central subspace or the central mean subspace.
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A martingale-difference-divergence-based estimation of central mean subspace

Statistics and Its Interface, 2019
Yu Zhang, Jicai Liu, Yuesong Wu, Xiangzhong Fang   +3 more
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