Spectral models for covariance matrices

By Robert J. Boik. 2002

80 citations recorded by the source corpus.

Subfields: Statistics and Probability, Signal Processing

View Paper: https://www.semanticscholar.org/paper/41b3f38f0fc319eb9c86e9d509f2664cab388b18

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Mohsen Pourahmadi
2. Clifford Lam
3. Trevor Park
4. Jianhua Z. Huang
5. Michael J. Daniels
6. Peter D. Hoff
7. N. Liu
8. Linxu Liu

In Signal Processing

1. Trevor Park
2. Mohsen Pourahmadi
3. Jianhua Z. Huang
4. Peter D. Hoff
5. N. Liu
6. Michael J. Daniels
7. Linxu Liu
8. Zongming Ma

Citations in the corpus

• A hierarchical eigenmodel for pooled covariance estimation (2008, methodology): “E-mail: pdhoff@u.washington.edu cases in which only certain columns or subspaces of U are shared across populations, and Boik (2002) described a very general model in which eigenspaces can be shared between all or some of the populations.”
• Simultaneous modelling of the Cholesky decomposition of several covariance matrices (2007, methodology): “Some of the most commonly used methods for handling several covariance matrices in the literature of multivariate statistics, the biomedical sciences, and financial econometrics are based on the spectral decomposition (Flury, 1984, 1988; Boik, 2002; Fraley and Raftery, 2002), the variancecorrelation”
• Covariance Estimation: The GLM and Regularization Perspectives (2011, methodology): “The local parametrization of orthogonal matrices in Boik (2002) could shed some light on the problem of interpretation of the new parameters. The idea of introducing matrix Bingham distributions as priors on the group of orthogonal matrices (Hoff, 2009) could also be useful in shrinking the eigenvec”
• High dimensional covariance matrix estimation using a factor model (2007, unknown): “See, for example, Dempster (1972), Leonard and Hsu (1992), Chiu, Leonard and Tsui (1996), Diggle and Verbyla (1998), Pourahmadi (2000), Boik (2002), Smith and Kohn (2002), Wong, Carter and Kohn (2003), Wu and Pourahmadi (2003), Huang, Liu and Pourahmadi (2004), and Li and Gui (2005).”
• Covariance matrix selection and estimation via penalised normal likelihood (2006, methodology): “Many existing methods deal directly with the individual elements of the covariance matrix; for a review of some of these methods see Diggle & Verbyla (1998), Diggle et al. (2002), Boik (2002) and Wong et al. (2003).”