Bayesian analysis of covariance matrices and dynamic models for longitudinal data
By Michael J. Daniels, Mohsen Pourahmadi. 2002
92 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/46ba22f48c09c3477e78a92b60e38d5848c34843
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wei Biao Wu
- Mohsen Pourahmadi
- Trevor Park
- Michael J. Daniels
- Bruce A. Craig
- Anindya Bhadra
- Chris Hans
- G. de los Campos
In Statistics and Probability
- Mohsen Pourahmadi
- Wei Biao Wu
- Trevor Park
- Michael J. Daniels
- Tommaso Cai
- Clifford Lam
- Harrison H. Zhou
- Andreas Hagemann
Citations in the corpus
- Simultaneous modelling of the Cholesky decomposition of several covariance matrices (2007, methodology): “As an alternative computational approach to obtain the MLE of the parameters in (19), one could use the iteratively reweighted least squares (IRLS) algorithm in Daniels and Pourahmadi (2002).”
- Nonparametric estimation of large covariance matrices of longitudinal data (2003, methodology): “In this follow-up paper to Pourahmadi (1999, 2000) and Daniels & Pourahmadi (2002), we provide nonparametric, positive-definite estimators of covariance matrices without assuming stationarity.”