Covariance reducing models: An alternative to spectral modelling of covariance matrices

• 成果类型: Article
• 署名作者: Cook, R. Dennis; Forzani, Liliana
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; National University of the Littoral; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn052
• 发表日期: 2008
• 页码: 799812
• 关键词: principal component subspaces

摘要

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.