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Employing a Monte Carlo algorithm in expectation maximization restricted maximum likelihood estimation of the linear mixed model.
MedLine Citation:
PMID:  23148971     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Multiple-trait and random regression models have multiplied the number of equations needed for the estimation of variance components. To avoid inversion or decomposition of a large coefficient matrix, we propose estimation of variance components by Monte Carlo expectation maximization restricted maximum likelihood (MC EM REML) for multiple-trait linear mixed models. Implementation is based on full-model sampling for calculating the prediction error variances required for EM REML. Performance of the analytical and the MC EM REML algorithm was compared using a simulated and a field data set. For field data, results from both algorithms corresponded well even with one MC sample within an MC EM REML round. The magnitude of the standard errors of estimated prediction error variances depended on the formula used to calculate them and on the MC sample size within an MC EM REML round. Sampling variation in MC EM REML did not impair the convergence behaviour of the solutions compared with analytical EM REML analysis. A convergence criterion that takes into account the sampling variation was developed to monitor convergence for the MC EM REML algorithm. For the field data set, MC EM REML proved far superior to analytical EM REML both in computing time and in memory need.
Authors:
K Matilainen; E A Mäntysaari; M H Lidauer; I Strandén; R Thompson
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-4-28
Journal Detail:
Title:  Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie     Volume:  129     ISSN:  1439-0388     ISO Abbreviation:  J. Anim. Breed. Genet.     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100955807     Medline TA:  J Anim Breed Genet     Country:  -    
Other Details:
Languages:  ENG     Pagination:  457-468     Citation Subset:  -    
Copyright Information:
© 2012 Blackwell Verlag GmbH.
Affiliation:
 MTT Agrifood Research Finland, Biotechnology and Food Research, Biometrical Genetics, Jokioinen, Finland  Rothamsted Research, Biomathematics and Bioinformatics, Harpenden, UK.
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