Assessing the pattern of covariance matrices via an augmentation multiple testing procedure

DOI10.1007/s10260-010-0157-5zbMath1232.62090OpenAlexW2109239032MaRDI QIDQ719007

Antonio Punzo, Salvatore Ingrassia, Francesca Greselin

Publication date: 27 September 2011

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10281/19052

zbMATH Keywords

augmentationspectral decompositionprincipal components analysishomoscedasticityF-G algorithm

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Paired and multiple comparisons; multiple testing (62J15)

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Model-based clustering via new parsimonious mixtures of heavy-tailed distributions ⋮ Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers ⋮ Testing equality of standardized generalized variances of \(k\) multivariate normal populations with arbitrary dimensions ⋮ Eigenvalues and constraints in mixture modeling: geometric and computational issues ⋮ On the Spectral Decomposition in Normal Discriminant Analysis ⋮ Unconstrained representation of orthogonal matrices with application to common principal components ⋮ Testing for equality of ordered eigenvectors of two multivariate normal populations ⋮ Performance evaluation of likelihood-ratio tests for assessing similarity of the covariance matrices of two multivariate normal populations ⋮ Hypothesis Testing for Mixture Model Selection ⋮ Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices ⋮ Stable and visualizable Gaussian parsimonious clustering models

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