Separable Covariance Matrices and Kronecker Approximation | Zendy

Raja P. Velu | Zendy; Kris Herman | Zendy

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Separable Covariance Matrices and Kronecker Approximation

Author(s) -

Raja P. Velu,

Kris Herman

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.05.184

Subject(s) - kronecker product , covariance , kronecker delta , covariance matrix , autoregressive model , separable space , computer science , mathematics , estimation of covariance matrices , positive definite matrix , series (stratigraphy) , covariance function , matrix (chemical analysis) , algorithm , econometrics , statistics , eigenvalues and eigenvectors , mathematical analysis , physics , quantum mechanics , paleontology , materials science , composite material , biology

When a model structure allows for the error covariance matrix to be written in the form of the Kronecker product of two positive definite covariance matrices, the estimation of the relevant parameters is intuitive and easy to carry out. In many time series models, the covariance matrix does not have a separable structure. Van Loan and Pitsanis (1993) provide an approximation with Kronecker products. In this paper, we apply their method to estimate the parameters of a multivariate regression model with autoregressive errors. An illustrative example is also provided.

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