Optimal rank-based tests for common principal components

DOI10.3150/12-BEJ461zbMath1457.62182OpenAlexW3121429754MaRDI QIDQ2435252

Thomas Verdebout, Davy Paindaveine, Marc Hallin

Publication date: 4 February 2014

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3150/12-bej461

zbMATH Keywords

robustnesslocal asymptotic normalitycommon principal componentsrank-based methods

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15)

Related Items

Efficient ANOVA for directional data ⋮ Semiparametric error-correction models for cointegration with trends: pseudo-Gaussian and optimal rank-based tests of the cointegration rank ⋮ Preliminary Multiple-Test Estimation, With Applications to k-Sample Covariance Estimation ⋮ Sign tests for weak principal directions ⋮ Limit theorem associated with Wishart matrices with application to hypothesis testing for common principal components ⋮ Optimal tests for elliptical symmetry: specified and unspecified location ⋮ Distribution and quantile functions, ranks and signs in dimension \(d\): a measure transportation approach ⋮ Efficient R-Estimation of Principal and Common Principal Components ⋮ Rank-based testing for semiparametric VAR models: a measure transportation approach

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Optimal rank-based testing for principal components
• A canonical definition of shape
• A Chernoff-Savage result for shape: On the non-admissibility of pseudo-Gaussian methods
• Optimal rank-based tests for homogeneity of scatter
• Robust tests for the common principal components model
• Asymptotic methods in statistical decision theory
• Robust plug-in estimators in proportional scatter models.
• Asymptotics in statistics. Some basic concepts.
• Semi-parametric efficiency, distribution-freeness and invariance
• Asymptotic theory for common principal component analysis
• On adaptive estimation in stationary ARMA processes
• Semiparametrically efficient rank-based inference for shape. I: optimal rank-based tests for sphericity
• Semiparametrically efficient rank-based inference for shape. II: Optimal \(R\)-estimation of shape
• Testing for Common Principal Components under Heterokurticity
• The decomposition of multitrait-multimethod matrices
• Parametric and semiparametric inference for shape: the role of the scale functional
• Analysis of Covariance Structures Under Elliptical Distributions
• Asymptotic distributions in canonical correlation analysis and other multivariate procedures for nonnormal populations
• Asymptotic Statistics
• A practical affine equivariant multivariate median
• Influence functions and outlier detection under the common principal components model: A robust approach
• Spectral models for covariance matrices
• Two generalizations of the common principal component model
• An Algorithm for Simultaneous Orthogonal Transformation of Several Positive Definite Symmetric Matrices to Nearly Diagonal Form
• The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses