Quotation Frühwirth-Schnatter, Sylvia, Tüchler, Regina. 2008. Bayesian parsimonious covariance estimation for hierarchical linear mixed models. Statistics and Computing 18 (1): 1-13.




We consider a non-centered parameterization of the standard random-effects model, which is based on the Cholesky decomposition of the variance-covariance matrix. The regression type structure of the non-centered parameterization allows us to use Bayesian variable selection methods for covariance selection. We search for a parsimonious variance-covariance matrix by identifying the non-zero elements of the Cholesky factors. With this method we are able to learn from the data for each effect whether it is random or not, and whether covariances among random effects are zero. An application in marketing shows a substantial reduction of the number of free elements in the variance-covariance matrix.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Statistics and Computing
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D
Language English
Title Bayesian parsimonious covariance estimation for hierarchical linear mixed models
Volume 18
Number 1
Year 2008
Page from 1
Page to 13
URL http://www.springerlink.com/content/0960-3174/?k=fr%c3%bchwirth-schnatter


Frühwirth-Schnatter, Sylvia (Details)
Tüchler, Regina
Institute for Statistics and Mathematics IN (Details)
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