High-dimensional sparse MANOVA
From MaRDI portal
Publication:406532
DOI10.1016/j.jmva.2014.07.002zbMath1298.62090OpenAlexW2040408175MaRDI QIDQ406532
Publication date: 8 September 2014
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2014.07.002
extreme value distributionlimiting null distributionprecision matrixMANOVAhigh dimensional testtesting equality of mean vectors
Hypothesis testing in multivariate analysis (62H15) Statistics of extreme values; tail inference (62G32)
Related Items
Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach, Testing linear hypothesis of high-dimensional means with unequal covariance matrices, Test on the linear combinations of mean vectors in high-dimensional data, Joint Mean and Covariance Estimation with Unreplicated Matrix-Variate Data, High-dimensional general linear hypothesis testing under heteroscedasticity, Test for mean matrix in GMANOVA model under heteroscedasticity and non-normality for high-dimensional data, A high-dimensional test for the k-sample Behrens–Fisher problem, High-Dimensional MANOVA Via Bootstrapping and Its Application to Functional and Sparse Count Data, Testing and support recovery of multiple high-dimensional covariance matrices with false discovery rate control, Likelihood ratio tests for elaborate covariance structures and for MANOVA models with elaborate covariance structures -- a review, Unnamed Item, High-dimensional rank-based inference, High-dimensional general linear hypothesis tests via non-linear spectral shrinkage, Comparing a large number of multivariate distributions, Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension, Linear hypothesis testing in high-dimensional one-way MANOVA, Testing regression coefficients in high-dimensional and sparse settings, Limiting behavior of eigenvalues in high-dimensional MANOVA via RMT, On testing the equality of high dimensional mean vectors with unequal covariance matrices, A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices, Sign-based test for mean vector in high-dimensional and sparse settings, Linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA: a normal reference \(L^2\)-norm based test, Recent developments in high-dimensional inference for multivariate data: parametric, semiparametric and nonparametric approaches, A test for the \(k\) sample Behrens-Fisher problem in high dimensional data, High-dimensional MANOVA under weak conditions, A high-dimensional test for multivariate analysis of variance under a low-dimensional factor structure, Novel multiplier bootstrap tests for high-dimensional data with applications to MANOVA, Moderate deviation principle for likelihood ratio test in multivariate linear regression model
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Innovated higher criticism for detecting sparse signals in correlated noise
- Shrinkage-based regularization tests for high-dimensional data with application to gene set analysis
- A test for the mean vector with fewer observations than the dimension under non-normality
- On convergence rates of suprema
- The asymptotic distribution and Berry-Esseen bound of a new test for independence in high dimension with an application to stochastic optimization
- A two-sample test for high-dimensional data with applications to gene-set testing
- Optimal classification in sparse Gaussian graphic model
- Some high-dimensional tests for a one-way MANOVA
- A test for the mean vector with fewer observations than the dimension
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- A Law of Large Numbers for the Maximum in a Stationary Gaussian Sequence
- Concerning a Certain Probability Problem
- Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
- Two-Sample Test of High Dimensional Means Under Dependence
- Multivariate Theory for Analyzing High Dimensional Data