Separable correlation and maximum likelihood
May 7, 2018
We consider estimation of the covariance matrix of a multivariate normal distribution when the correlation matrix is separable in the sense that it factors as a Kronecker product of two smaller matrices. A computationally convenient coordinate descent-type algorithm is developed for maximum likelihood estimation. Simulations indicate our method often gives smaller estimation error than some common alternatives when correlation is separable, and that correctly sized tests for correlation separability can be obtained using a parametric bootstrap. Using dissolved oxygen data from the Upper Mississippi River, we illustrate how our model can lead to interesting scientific findings that may be missed when using competing models.
Citation Information
Publication Year | 2018 |
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Title | Separable correlation and maximum likelihood |
DOI | 10.48550/arXiv.1805.00318 |
Authors | Karl Oskar Ekvall, Brian R. Gray |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | arXiv |
Index ID | 70255991 |
Record Source | USGS Publications Warehouse |
USGS Organization | Upper Midwest Environmental Sciences Center |