Unconstrained parametrizations for variance-covariance matrices

Volume: 6, Issue: 3, Pages: 289 - 296
Published: Sep 1, 1996
Abstract
The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness,...
Paper Details
Title
Unconstrained parametrizations for variance-covariance matrices
Published Date
Sep 1, 1996
Volume
6
Issue
3
Pages
289 - 296
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.