Multivariate Two-Sided Tests for Normal Mean Vectors with Unknown Covariance Matrix

DOI10.1080/03610918.2011.633727zbMath1328.62353OpenAlexW1967100969MaRDI QIDQ5299816

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Publication date: 21 June 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.633727

zbMATH Keywords

orthogonal projectionpower of test\(\beta\)-distribution

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Parametric inference under constraints (62F30)

Cites Work

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• Testing homogeneity of multivariate normal mean vectors under an order restriction when the covariance matrices are common but unknown
• Accurate Critical Constants for the One-Sided Approximate Likelihood Ratio Test of a Normal Mean Vector When the Covariance Matrix Is Estimated
• Multivariate Two-Sided Tests for Normal Mean Vectors Based on Approximations of Likelihood Ratio Test
• An approximate likelihood ratio test for a normal mean vector with nonnegative components with application to clinical trials
• MULTIVARIATE TESTS OF NORMAL MEAN VECTORS WITH RESTRICTED ALTERNATIVES
• One-Sided Testing Problems in Multivariate Analysis

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