Results 201 to 210 of about 8,432 (220)

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.
Peng Zeng
exaly   +5 more sources

Fused clustering mean estimation of central subspace

Journal of the Korean Statistical Society, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Um, Hye Yeon, Yoo, Jae Keun
exaly   +5 more sources

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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.
Pei Wang, Xiangrong Yin, Qingcong Yuan, Richard J. Kryscio   +3 more
openaire   +3 more sources

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.
Liping Zhu, Wei Zhong
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Dimension reduction estimation for central mean subspace with missing multivariate response

Journal of Multivariate Analysis, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guoliang Fan 0002, Hong-xian Xu, Han-Ying Liang   +2 more
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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.
openaire   +4 more sources

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
openaire   +3 more sources

Sufficient Dimension Folding for Regression Mean Function

Journal of Computational and Graphical Statistics, 2014
In this article, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded ...
Xiangrong Yin
exaly   +2 more sources