Covariance reducing models: An alternative to spectral modelling of covariance matrices
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 p...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/84236 |
| Acceso en línea: | http://hdl.handle.net/11336/84236 |
| Access Level: | acceso abierto |
| Palabra clave: | CENTRAL SUBSPACE DIMENSION REDUCTION ENVELOPES GRASSMANN MANIFOLDS REDUCING SUBSPACES https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | 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. |
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