Statistical inference on the significance of rows and columns for matrix-valued data in an additive model
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Publication:6064232
DOI10.1007/s11749-023-00852-3zbMath1527.62039OpenAlexW4324056403MaRDI QIDQ6064232
Lu Niu, Xiu-Min Liu, Jun-Long Zhao
Publication date: 12 December 2023
Published in: Test (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11749-023-00852-3
hypothesis testingconfidence intervalvariable screeningmatrix-valued datarow and column significance
Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15)
Cites Work
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- Tensor-on-Tensor Regression
- Covariate-Adjusted Tensor Classification in High-Dimensions
- Comparison of ICC and CCC for assessing agreement for data without and with replications
- Trace regression model with simultaneously low rank and row(column) sparse parameter
- Cross: efficient low-rank tensor completion
- Robust covariance estimation for approximate factor models
- Covariance estimation via sparse Kronecker structures
- The control of the false discovery rate in multiple testing under dependency.
- Multilayer tensor factorization with applications to recommender systems
- Robust estimator of the correlation matrix with sparse Kronecker structure for a high-dimensional matrix-variate
- On dimension folding of matrix- or array-valued statistical objects
- Convex regularization for high-dimensional multiresponse tensor regression
- The fused Kolmogorov filter: a nonparametric model-free screening method
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Multiscale Adaptive Regression Models for Neuroimaging Data
- L2RM: Low-Rank Linear Regression Models for High-Dimensional Matrix Responses
- Tensor SVD: Statistical and Computational Limits
- Hommel's procedure in linear time
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Model-Free Variable Selection With Matrix-Valued Predictors
- Regularized Matrix Regression
- A Bayesian approach to joint modeling of matrix‐valued imaging data and treatment outcome with applications to depression studies
- Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data
- High dimensional semiparametric estimate of latent covariance matrix for matrix-variate
- Intrinsic Regression Models for Positive-Definite Matrices With Applications to Diffusion Tensor Imaging
- Tensor Regression with Applications in Neuroimaging Data Analysis
- Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
- Scalar-on-image regression via the soft-thresholded Gaussian process
- Structured lasso for regression with matrix covariates
- Provable Sparse Tensor Decomposition
- Nonparametric matrix response regression with application to brain imaging data analysis
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