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- W996867656 abstract "Improvements in computing algorithms, in particular the use of the Average Information (AI) algorithm for estimating variance parameters in mixed models have encouraged researchers to fit more complex models to larger sets of data. The AI algorithm does not naturally force unstructured matrices to be positive definite and new approaches are required if this condition must be imposed. Another problem arises with fitting large and complex models when the computer runs out of memory. While model reformulation, getting a bigger computer and improvements to existing computing algorithms may go some way to addressing this issue, significant progress will not be made until new strategies are developed. The paper reviews several strategies for fitting bigger models including one called imputation which is to be implemented in ASReml. Imputation uses ideas borrowed from Gibbs sampling to break the model into sections which are fitted separately." @default.
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- W996867656 date "2003-01-01" @default.
- W996867656 modified "2023-09-27" @default.
- W996867656 title "Options for estimating variance components in large mixed models." @default.
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