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A New Algorithm for Explicit Determination of Variance Component Estimations in Mixed Models and Mixed Classifications of Balanced ANOVA
Author(s) -
Holomek D.
Publication year - 1978
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710200503
Subject(s) - mathematics , variance components , variance (accounting) , statistics , one way analysis of variance , analysis of variance , mixed model , variance function , variance based sensitivity analysis , confidence interval , variation (astronomy) , simple (philosophy) , algorithm , regression analysis , accounting , physics , astrophysics , business , philosophy , epistemology
This paper deals with the balanced case of the analysis of variance. The use of a classification function leads to an easy determination of all possible sources of variation of any mixed classification. For mixed models a new method is derived, which allows to represent explicit the ANOVA‐estimations of the variance components respectively the estimation of the mean sum of squares of the fixed effects for all sources of variation. Thereby the corresponding F ‐quotients and the approximate confidence intervals of variance components are received in a simple way.