Sign-based test for mean vector in high-dimensional and sparse settings
From MaRDI portal
Publication:2287782
DOI10.1007/s10114-019-8290-zzbMath1435.62200OpenAlexW3000265829MaRDI QIDQ2287782
Publication date: 21 January 2020
Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10114-019-8290-z
high-dimensional datatesting mean vectormaximum type testsign-based dense testsign-based sparsity testsum of squares type test
Nonparametric robustness (62G35) Hypothesis testing in multivariate analysis (62H15) Statistical aspects of big data and data science (62R07)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Innovated higher criticism for detecting sparse signals in correlated noise
- High-dimensional sparse MANOVA
- Central limit theorem for Hotelling's \(T^{2}\) statistic under large dimension
- Semiparametrically efficient inference based on signed ranks in symmetric independent component models
- Multivariate nonparametric methods with R. An approach based on spatial signs and ranks.
- A test for the mean vector with fewer observations than the dimension under non-normality
- On the Gaussian approximation of convolutions under multidimensional analogues of S. N. Bernstein's inequality conditions
- A test for the mean vector in large dimension and small samples
- 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
- Properties of higher criticism under strong dependence
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
- Two-Sample Test of High Dimensional Means Under Dependence
- A High-Dimensional Nonparametric Multivariate Test for Mean Vector
This page was built for publication: Sign-based test for mean vector in high-dimensional and sparse settings