Comparison of two computing algorithms for solving mixed model equations for multiple trait random regression test day models

Livestock Production Science

Volumn 67, Issue 1-2, 2000, Pages 143-153

  Jamrozik, J ^a   Schaeffer, L R ^a  

^a UNIVERSITY OF GUELPH   (Canada)

Author keywords

Computing; Genetic evaluation; Test day models

Indexed keywords

EID: 0033665294     PISSN: 03016226     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0301-6226(00)00186-X     Document Type: Article

References (17)

1. Solution of multiple trait animal models with missing data on some traits
2. Iterative solving of random regression models by sequential estimation of regressions and effects affecting regressions
3. Genetic evaluation of dairy cattle using test day yields and random regression model
4. Genetic parameters for production traits and somatic cell score of Canadian Holsteins with multiple trait random regression model
5. Fast and flexible program for genetic evaluation in dairy cattle
6. A parallel solver for multi-trait animal models
7. Computing inbreeding coefficients in large populations
8. Complex models, more data: Simpler programming
9. Use of test day yields for genetic evaluation of dairy sires and cows
10. Genetic evaluation for somatic cell score with a test day model for multiple lactations
11. Computing strategies for solving mixed model equations
12. Experience with a test day model
13. Estimating daily yields of cows from different milking schemes
14. Parallel benefits in test-day evaluations
15. Predicting breeding values using an implicit representation of the mixed model equations for a multiple trait animal model
16. The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records
17. Adjustment of test day milk, fat, and protein yield for age, season and stage of lactation