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CHOLESKY DECOMPOSITION OF A VARIANCE MATRIX IN REPEATED MEASURES ANALYSIS
Author(s) -
Lianto S.,
McGilchrist C.A.
Publication year - 1988
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1988.tb00853.x
Subject(s) - cholesky decomposition , minimum degree algorithm , decomposition , restricted maximum likelihood , variance (accounting) , matrix (chemical analysis) , inverse , variance decomposition of forecast errors , incomplete cholesky factorization , statistics , mathematics , matrix decomposition , covariance matrix , econometrics , maximum likelihood , chemistry , chromatography , economics , eigenvalues and eigenvectors , physics , organic chemistry , accounting , quantum mechanics , geometry
Summary The Cholesky decomposition is given for the inverse of a variance matrix occurring in repeated measures problems where observations have a correlation structure both within and between experimental units. The use of this decomposition is outlined for ML and REML estimation procedures.