Parallel restricted maximum likelihood estimation for linear models with a dense exogenous matrix

Restricted maximum likelihood (REML) estimation of variance–covariance matrices is an optimization problem that has both scientific and industrial applications. Parallel REML gradient algorithms are presented and compared for linear models whose covariance matrix is large, sparse and possibly unstru...

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Bibliographische Detailangaben

Veröffentlicht in:	Parallel computing 2002-02, Vol.28 (2), p.343-353
1. Verfasser:	Malard, Joël M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Covariance matrix estimation Linear models Numerical optimization Parallel computing Restricted maximum likelihood
Online-Zugang:	Volltext
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