Tests for parallelism and flatness hypotheses of two mean vectors in high-dimensional settings
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Publication:5222399
DOI10.1080/00949655.2015.1055269OpenAlexW2227493658MaRDI QIDQ5222399
Nobumichi Shutoh, Sho Takahashi
Publication date: 1 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2015.1055269
asymptotic expansionMonte Carlo simulationhigh-dimensional dataCauchy-Schwarz inequalityprofile analysis
Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15)
Related Items (4)
Tests for the parallelism and flatness hypotheses of multi-group profile analysis for high-dimensional elliptical populations ⋮ Test for mean matrix in GMANOVA model under heteroscedasticity and non-normality for high-dimensional data ⋮ Profile analysis in high dimensions ⋮ Accurate inference for repeated measures in high dimensions
Cites Work
- Testing linear hypotheses of mean vectors for high-dimension data with unequal covariance matrices
- Asymptotic expansions of the null distributions of some test statistics for profile analysis in general distributions
- Asymptotic expansions of the null distributions for the Dempster trace criterion
- Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
- On methods in the analysis of profile data
- Multiple Comparisons Among Mean Vectors When the Dimension is Larger Than the Total Sample Size
- On the Distributions of some Test Statistics for Profile Analysis in Elliptical Populations
- Profile analysis of several groups
- A generalized multivariate analysis of variance model useful especially for growth curve problems
- Multivariate Statistics
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