Tests for mean vectors in high dimension
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Publication:2870766
DOI10.1002/sam.11209OpenAlexW1592078486MaRDI QIDQ2870766
Ivair R. Silva, Edgard M. Maboudou-Tchao
Publication date: 21 January 2014
Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/sam.11209
Related Items (4)
A generalized likelihood ratio test for normal mean when \(p\) is greater than \(n\) ⋮ High-dimensional data monitoring using support machines ⋮ Bayesian Monte Carlo testing with one-dimensional measures of evidence ⋮ Frequentist-Bayesian Monte Carlo test for mean vectors in high dimension
Cites Work
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- Sparse inverse covariance estimation with the graphical lasso
- A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
- A well-conditioned estimator for large-dimensional covariance matrices
- Models with a Kronecker product covariance structure: estimation and testing
- Modified linear discriminant analysis approaches for classification of high-dimensional microarray data
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- A two-sample test for high-dimensional data with applications to gene-set testing
- A test for the mean vector with fewer observations than the dimension
- Array Variate Random Variables with Multiway Kro- necker Delta Covariance Matrix Structure
- Two-Stage Procedures for High-Dimensional Data
- Model selection and estimation in the Gaussian graphical model
- First-Order Methods for Sparse Covariance Selection
- Power of the Sequential Monte Carlo Test
- Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices
- Statistical Applications of the Multivariate Skew Normal Distribution
- The multivariate skew-normal distribution
- Multivariate Theory for Analyzing High Dimensional Data
- A Significance Test for the Separation of Two Highly Multivariate Small Samples
- A High Dimensional Two Sample Significance Test
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