A test for the mean vector with fewer observations than the dimension

2006/12/15 by Muni S. Srivastava, Meng Du · 21 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Gene expression and cancer classification #Bioinformatics and Genomic Networks #Algorithms and Data Compression

paper · doi:10.1016/j.jmva.2006.11.002

Citations

• A test for the mean vector with fewer observations than the dimension under non-normality

Cited by

• A two sample test in high dimensional data
• A test for the mean vector with fewer observations than the dimension under non-normality
• Testing high-dimensional means under sparse missing pattern
• Hypothesis testing for high-dimensional time series via self-normalization
• Asymptotically independent U-statistics in high-dimensional testing
• Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data
• Likelihood-based tests on linear hypotheses of large dimensional mean vectors with unequal covariance matrices
• Optimal Sign Test for High Dimensional Location Parameters
• A Bayes-Motivated Quadratic-Form Test for High-Dimensional Mean Testing
• High dimensional Mean Test for Temporal Dependent Data
• Mean vector testing for high dimensional dependent observations
• An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
• A fast and effective kernel two-sample test for large-scale data
• A test for k sample Behrens-Fisher problem in high dimensional data
• On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives
• Significance Analysis of High-Dimensional, Low-Sample Size Partially Labeled Data
• Exact Multivariate Tests - A New Effective Principle of Controlled Model\n Choice
• A More Powerful Two-Sample Test in High Dimensions using Random\n Projection
• Hypothesis Testing of One-Sample Mean Vector in Distributed Frameworks
• Mean Test with Fewer Observation than Dimension and Ratio Unbiased Estimator for Correlation Matrix
• Spatial-Sign based High-Dimensional Location Test