Dimension reduction for covariance matrices

LILIANA FORZANI; COOK, R. D.

Biometrika

Año: 2008 p. 1 - 1

0006-3444

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.