An efficient computing strategy for prediction in mixed linear models

A - Papers appearing in refereed journals

Gilmour, A., Cullis, B. R., Welham, S. J., Gogel, B. and Thompson, R. 2004. An efficient computing strategy for prediction in mixed linear models. Computational Statistics & Data Analysis. 44 (4), pp. 571-586.

AuthorsGilmour, A., Cullis, B. R., Welham, S. J., Gogel, B. and Thompson, R.
Abstract

After estimation of effects from a linear mixed model, it is often useful to form predicted values for certain factor/variate combinations. This process has been well-defined for linear models, but the introduction of random effects means that a decision has to be made about the inclusion or exclusion of random model terms from the predictions, including the residual error. For spatially correlated data, kriging then becomes prediction from the fitted model. In many cases, the size of the matrices required to calculate predictions and their covariance matrix directly can be prohibitive. An efficient computational strategy for calculating predictions and their standard errors is given, which includes the ability to detect the invariance of predictions to the parameterisation used in the model.

KeywordsREML; BLUP; Linear mixed models; Prediction
Year of Publication2004
JournalComputational Statistics & Data Analysis
Journal citation44 (4), pp. 571-586
Digital Object Identifier (DOI)doi:10.1016/S0167-9473(02)00258-X
Open accessPublished as bronze (free) open access
Funder project or code445
513
Research in statistics relevant to biological processes
Publisher's version
Output statusPublished
Publication dates
Online24 Oct 2002
Copyright licensePublisher copyright
PublisherElsevier Science Bv
ISSN0167-9473

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