A Normality Test for High-dimensional Data Based on the Nearest Neighbor Approach
From MaRDI portal
Publication:6107242
DOI10.1080/01621459.2021.1953507zbMath1514.62085arXiv1904.05289OpenAlexW3182454004MaRDI QIDQ6107242
No author found.
Publication date: 3 July 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1904.05289
Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Generalization of Shapiro–Wilk's Test for Multivariate Normality
- Sparse inverse covariance estimation with the graphical lasso
- Sequential multi-sensor change-point detection
- Gaussian graphical model estimation with false discovery rate control
- Exact post-selection inference, with application to the Lasso
- Valid post-selection inference
- Nonparametric multivariate rank tests and their unbiasedness
- Influential features PCA for high dimensional clustering
- Optimal detection of multi-sample aligned sparse signals
- High-dimensional classification using features annealed independence rules
- A multivariate two-sample test based on the number of nearest neighbor type coincidences
- A consistent test for multivariate normality based on the empirical characteristic function
- Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
- A new approach to the BHEP tests for multivariate normality
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- Sparse permutation invariant covariance estimation
- Network exploration via the adaptive LASSO and SCAD penalties
- Graph-based change-point detection
- Sequential change-point detection based on nearest neighbors
- Regularized estimation of large covariance matrices
- A direct approach to sparse discriminant analysis in ultra-high dimensions
- A Direct Estimation Approach to Sparse Linear Discriminant Analysis
- Some Techniques for Assessing Multivarate Normality Based on the Shapiro- Wilk W
- Model selection and estimation in the Gaussian graphical model
- Multivariate Two-Sample Tests Based on Nearest Neighbors
- A Multidimensional Goodness-of-Fit Test Based on Interpoint Distances
- A Weighted Edge-Count Two-Sample Test for Multivariate and Object Data
- High Dimensional Change Point Estimation via Sparse Projection
- Scalable SUM-Shrinkage Schemes for Distributed Monitoring Large-Scale Data Streams
- Post‐selection inference for ‐penalized likelihood models
- Two-Sample Test of High Dimensional Means Under Dependence
- A powerful test for multivariate normality
- Testing differential networks with applications to the detection of gene-gene interactions
- An analysis of variance test for normality (complete samples)
- Measures of multivariate skewness and kurtosis with applications
This page was built for publication: A Normality Test for High-dimensional Data Based on the Nearest Neighbor Approach