Asymptotic Expansion of the Null Distribution of LR Statistic for Multivariate Linear Hypothesis when the Dimension is Large
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Publication:3155321
DOI10.1081/STA-120029835zbMath1114.62315OpenAlexW2024669733MaRDI QIDQ3155321
Tetsuji Tonda, Yasunori Fujikoshi
Publication date: 14 January 2005
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1081/sta-120029835
Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15)
Related Items (15)
Contributions to multivariate analysis by Professor Yasunori Fujikoshi ⋮ Edgeworth expansion of Wilks' lambda statistic ⋮ High-Dimensional Edgeworth Expansion of LR Statistic for Testing Circular Symmetric Covariance Structure and Its Error Bound ⋮ Group feature screening via the F statistic ⋮ Likelihood ratio tests for elaborate covariance structures and for MANOVA models with elaborate covariance structures -- a review ⋮ Computable Error Bounds for High-Dimensional Approximations of an LR Statistic for Additional Information in Canonical Correlation Analysis ⋮ MANOVA for large hypothesis degrees of freedom under non-normality ⋮ Some high-dimensional tests for a one-way MANOVA ⋮ A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices ⋮ Berry-Esseen bound for high dimensional asymptotic approximation of Wilks' lambda distribution ⋮ High-dimensional multivariate repeated measures analysis with unequal covariance matrices ⋮ Asymptotic expansions of test statistics for dimensionality and additional information in canonical correlation analysis when the dimension is large ⋮ Asymptotics for tests on mean profiles, additional information and dimensionality under non-normality ⋮ High-dimensional asymptotic expansion of LR statistic for testing intraclass correlation structure and its error bound ⋮ Moderate deviation principle for likelihood ratio test in multivariate linear regression model
Cites Work
- Saddlepoint method for obtaining tail probability of Wilk's likelihood ratio test
- Error bounds for asymptotic approximations of the linear discriminant function when the sample sizes and dimensionality are large
- A normal approximation for the distribution of the likelihood ratio statistic in multivariate analysis of variance
- Saddlepoint approximations for the generalized variance and Wilks' statistic
- Some results on the distribution of Wilks's likelihood-ratio criterion
- Asymptotic aproximations for EPMC’s of the linear and the quadratic discriminant functions when the sample sizes and the dimension are large
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