Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSolomon, P.-
dc.contributor.authorTaylor, J.-
dc.identifier.citationBiometrika, 1999; 86(2):289-300-
dc.description.abstractWe consider variance components and other models for repeated measures in which a general transformation is applied to the response variable. Using Cox & Reid's (1987) concept of parameter orthogonality and some approximations to the information matrix we show that the intraclass correlation coefficient in the one-way model is robust to the choice of transformation. This robustness result generalises to a vector of parameters determining the correlation structure, to more complex variance components models, to multivariate normal models, to some longitudinal models and models involving linear regression functions, for which we show that ratios of regression parameters are robustly estimated. The results suggest that a natural way to parameterise the covariance structure in repeated measures models may be in terms of the variance and the correlation determined by separate sets of parameters. © 1999 Biometrika Trust.-
dc.titleOrthogonality and transformations in variance components models-
dc.typeJournal article-
dc.identifier.orcidSolomon, P. [0000-0002-0667-6947]-
Appears in Collections:Applied Mathematics publications
Aurora harvest 6

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.