Separable Covariance Matrices and Kronecker Approximation

Raja Velu, Kris Herman

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1019-1029
Number of pages11
JournalProcedia Computer Science
Volume108
DOIs
StatePublished - 2017
EventInternational Conference on Computational Science ICCS 2017 - Zurich, Switzerland
Duration: Jun 12 2017Jun 14 2017

Keywords

  • approximation of covariance matrices
  • dimension-reduction
  • multivariate regression

ASJC Scopus subject areas

  • Computer Science(all)

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