Performance evaluation of likelihood-ratio tests for assessing similarity of the covariance matrices of two multivariate normal populations

DOI10.1080/03610926.2013.863933zbMath1341.62147OpenAlexW2340769573MaRDI QIDQ2807776

Dariush Najarzadeh, Mojtaba Khazaei, Mojtaba Ganjali

Publication date: 25 May 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.863933

zbMATH Keywords

sizespectral decompositionLRTFlury-Gautschi algorithmshape and direction parameters

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Hypothesis testing in multivariate analysis (62H15)

Related Items (1)

Testing equality of generalized variances of k multivariate normal populations

Cites Work

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• Optimal rank-based testing for principal components
• Assessing the pattern of covariance matrices via an augmentation multiple testing procedure
• Unbiasedness of the likelihood ratio tests for equality of several covariance matrices and equality of several multivariate normal populations
• Testing for Common Principal Components under Heterokurticity
• A test of the hypothesis of partial common principal components
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• An Algorithm for Simultaneous Orthogonal Transformation of Several Positive Definite Symmetric Matrices to Nearly Diagonal Form
• Measures of multivariate skewness and kurtosis with applications

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