A Pairwise Hotelling Method for Testing High-Dimensional Mean Vectors
From MaRDI portal
Publication:6185128
DOI10.5705/ss.202021.0369arXiv2003.04636OpenAlexW3011011105WikidataQ114013774 ScholiaQ114013774MaRDI QIDQ6185128
Marc G. Genton, Tie Jun Tong, Zongliang Hu
Publication date: 29 January 2024
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.04636
high-dimensional datascreeningstatistical powerHotelling's testpairwise correlationtype-i error rate
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A two sample test in high dimensional data
- An adaptable generalization of Hotelling's $T^2$ test in high dimension
- Robust rank correlation based screening
- A note on high-dimensional two-sample test
- Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data
- A test for the mean vector with fewer observations than the dimension under non-normality
- A high-dimensional two-sample test for the mean using random subspaces
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- On the distribution of the largest eigenvalue in principal components analysis
- A test for the mean vector in large dimension and small samples
- Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes
- Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model
- Mean vector testing for high-dimensional dependent observations
- A two-sample test for high-dimensional data with applications to gene-set testing
- Two-sample and ANOVA tests for high dimensional means
- A \(U\)-statistic approach for a high-dimensional two-sample mean testing problem under non-normality and Behrens-Fisher setting
- A test for the mean vector with fewer observations than the dimension
- Asymptotically independent U-statistics in high-dimensional testing
- A Powerful Bayesian Test for Equality of Means in High Dimensions
- A Regularized Hotelling’sT2Test for Pathway Analysis in Proteomic Studies
- A note on pseudolikelihood constructed from marginal densities
- A Multivariate Two-Sample Mean Test for Small Sample Size and Missing Data
- Two-sample behrens-fisher problem for high-dimensional data
- Two-sample tests for high-dimension, strongly spiked eigenvalue models
- Two-Sample Test of High Dimensional Means Under Dependence
- Empirical likelihood test for a large-dimensional mean vector
- A Simple Two-Sample Test in High Dimensions Based on L2-Norm
- Diagonal likelihood ratio test for equality of mean vectors in high‐dimensional data
- Composite $T^2$ test for high-dimensional data
- A Two-Sample Test for Equality of Means in High Dimension
- An adaptive two-sample test for high-dimensional means
- Two-sample tests of high-dimensional means for compositional data