We introduce covariance reducing models for studying the sample covariance matrices of a random vector observed in different populations. The models are based on reducing the sample covariance matrices to an informational core that is sufficient to characterize the variance heterogeneity among the populations. They possess useful equivariance properties and provide a clear alternative to spectral models for covariance matrices.Fil: Cook, R. Dennis. University of Minnesota; Estados UnidosFil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Li...
Abstract: The limiting spectral distribution of large sample covariance matrices is derived under de...
International audienceWe consider the problem of estimating the covariance matrix Mp of an observati...
Estimating large covariance matrices from small samples is an important problem in many fields. Amon...
Cook and Forzani (2008) proposed covariance reducing models as a method for modeling the differences...
Cook and Forzani (2008) proposed covariance reducing models as a method for modeling the differences...
Abstract—In many practical situations we would like to es-timate the covariance matrix of a set of v...
Sample covariance matrices play a central role in numerous popular statistical methodologies, for ex...
<p>(A) Eigenvalue distribution of an example population covariance matrix () computed from the van ...
SUMMARY. A new method is given to obtain an estimator for the covariance matrix of the response vari...
Multivariate estimation fitting a common structure to estimates of genetic and environmental covaria...
Abstract. Estimating covariance matrices is an important part of port-folio selection, risk manageme...
Multivariate statistical analyses, such as linear discriminant analysis, MANOVA, and profile analysi...
We explore simultaneous modeling of several covariance matrices across groups using the spectral (ei...
41 pages, 1 article*Generalized Covariance Matrices for Variance Components Models* (Searle, S. R.; ...
Covariance matrix estimation plays an important role in statistical analysis in many fields, includi...
Abstract: The limiting spectral distribution of large sample covariance matrices is derived under de...
International audienceWe consider the problem of estimating the covariance matrix Mp of an observati...
Estimating large covariance matrices from small samples is an important problem in many fields. Amon...
Cook and Forzani (2008) proposed covariance reducing models as a method for modeling the differences...
Cook and Forzani (2008) proposed covariance reducing models as a method for modeling the differences...
Abstract—In many practical situations we would like to es-timate the covariance matrix of a set of v...
Sample covariance matrices play a central role in numerous popular statistical methodologies, for ex...
<p>(A) Eigenvalue distribution of an example population covariance matrix () computed from the van ...
SUMMARY. A new method is given to obtain an estimator for the covariance matrix of the response vari...
Multivariate estimation fitting a common structure to estimates of genetic and environmental covaria...
Abstract. Estimating covariance matrices is an important part of port-folio selection, risk manageme...
Multivariate statistical analyses, such as linear discriminant analysis, MANOVA, and profile analysi...
We explore simultaneous modeling of several covariance matrices across groups using the spectral (ei...
41 pages, 1 article*Generalized Covariance Matrices for Variance Components Models* (Searle, S. R.; ...
Covariance matrix estimation plays an important role in statistical analysis in many fields, includi...
Abstract: The limiting spectral distribution of large sample covariance matrices is derived under de...
International audienceWe consider the problem of estimating the covariance matrix Mp of an observati...
Estimating large covariance matrices from small samples is an important problem in many fields. Amon...