Prediction of random effects in finite mixture models with Gaussian components

Journal of Animal Breeding and Genetics

Volumn 122, Issue 3, 2005, Pages 145-160

  Gianola, D ^a,b,c  

^a UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^b NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF VARIANCE; ANIMAL; ANIMAL MODEL; ARTICLE; BIOLOGICAL MODEL; CLUSTER ANALYSIS; NORMAL DISTRIBUTION; STATISTICAL MODEL;

ANALYSIS OF VARIANCE; ANIMALS; CLUSTER ANALYSIS; MODELS, ANIMAL; MODELS, GENETIC; MODELS, STATISTICAL; NORMAL DISTRIBUTION;

ANIMALIA;

EID: 19944412494     PISSN: 09312668     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1439-0388.2005.00529.x     Document Type: Article

References (41)

1. Aitkin M., Rubin D.B. (1985) Estimation and hypothesis testing in finite mixture models. J. R. Stat. Soc. Ser. B, 47, 67-75.
2. Broemeling L.D. (1985) Bayesian Analysis of Linear Models. Marcel Dekker, New York, NY.
3. Brown H., Prescott R. (1999) Applied Mixed Models in Medicine. Wiley, Chichester, UK.
4. Bulmer M.G. (1980) The Mathematical Theory of Quantitative Genetics. Clarendon Press, Oxford, UK.
5. Crow J.F., Kimura M. (1970) Introduction to Population Genetics Theory. Harper and Row, New York, NY.
6. Dempster A.P., Laird N.M., Rubin D.B. (1997) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. R. Stat. Soc. Ser. B, 39, 1-38.
7. Detilleux J., Leroy P.L. (2000) Application of a mixed normal mixture model to the estimation of mastitis-related parameters. J. Dairy Sci., 83, 2341-2349.
8. Fernando R.L., Gianola D. (1986) Optimal properties of the conditional mean as a selection criterion. Theor. Appl. Genet., 72, 822-825.
9. Gianola D. (2001) Inferences about breeding values. In: D.J. Balding, M. Bishop, C. Cannings (eds), Handbook of Statistical Genetics, Wiley, Buffins Lane, pp. 645-672.
10. Gianola D., Fernando R.L. (1986) Bayesian methods in animal breeding theory. J. Anim. Sci., 63, 217-244.
11. Gianola D., Ødegård J., Heringstad B., Sorensen D., Madsen P., Jensen J., Detilleux J. (2004) Mixture model for inferring susceptibility to mastitis in dairy cattle: a procedure for likelihood-based inference. Genet. Sel. Evol., 36, 3-27.
12. Goldberger A.S. (1962) Best linear unbiased prediction in the generalized linear regression model. J. Am. Stat. Assoc., 57, 369-375.
13. Henderson C.R. (1950) Estimation of genetic parameters (Abstract). Ann. Math. Stat., 21, 309-310.
14. Henderson C.R. (1953) Estimation of variance and covariance components. Biometrics, 9, 226-252.
15. Henderson C.R. (1973) Sire evaluation and genetic trends. In: Proceedings of the Animal Breeding and Genetics Symposium in Honor of Dr Jay L. Lush, American Society of Animal Science and American Dairy Science Association, Champaign, IL, pp. 10-41.
16. Henderson C.R. (1976) A simple method for computing the inverse of a numerator relationship matrix used for prediction of breeding values. Biometrics, 32, 69-86.
17. Henderson C.R. (1984) Applications of Linear Models in Animal Breeding. University of Guelph, Guelph, ON.
18. Heringstad B., Klemetsdal G., Ruane J. (2000) Selection for mastitis resistance in dairy cattle: a review with focus on the situation in the Nordic countries. Livest. Prod. Sci., 64, 95-106.
19. Laird N.M., Ware J.H. (1982) Random effects models for longitudinal data. Biometrics, 38, 963-974.
20. Lindley D.V., Smith A.F.M. (1972) Bayes estimates for the linear model (with discussion). J. R. Stat. Soc. Ser. B, 37, 23-37.
21. McLachlan G., Peel D. (2000) Finite Mixture Models. Wiley, New York, NY.
22. Militino A.F., Ugarte M.D., Fean C.B. (2001) The use of mixture models for identifying high risks in disease mapping. Stat. Med., 20, 2035-2049.
23. Muthén B., Shedden K. (1999) Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
24. Newton M.A., Kendziorski C.M., Richmond C.S., Blattner F.R., Tsui K.W. (2001) On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol., 8, 37-52.
25. Nguyen D.V., Arpat A.B., Wang N., Carroll R.J. (2002) DNA microarray experiments: biological and technological aspects. Biometrics, 58, 701-717.
26. Ødegård J., Jensen J., Madsen P., Gianola D., Klemetsdal G., Heringstad B. (2003) Mixture models for detection of mastitis in dairy cattle using test-day somatic cell scores: a Bayesian approach via Gibbs sampling. J. Dairy Sci., 86, 3694-3703.
27. Pauler D.K., Laird N.M. (2000) A mixture model for longitudinal data with application to assessment of noncompliance. Biometrics, 56, 464-472.
28. Pearson K. (1894) Contributions to the mathematical theory of evolution. Phil. Trans. Roy. Soc. A, 185, 71-110.
29. Quaas R.L. (1988) Additive genetic model with groups and relationships. J. Dairy Sci., 71, 1338-1345.
30. Robert C.P. (1996) Mixtures of distributions: inference and estimation. In: W.R. Gilks, S. Richardson, D.J. Spiegelhalter (eds), Markov Chain Monte Carlo in Practice, Chapman and Hall, London, UK, pp. 441-464.
31. Robinson G.K. (1991) That BLUP is a good thing: the estimation of random effects. Stat. Sci., 6, 15-51.
32. Searle S.R. (1971) Linear Models. Wiley, New York, NY.
33. Searle S.R., Casella G., McCulloch C.E. (1992) Variance Components. Wiley, New York, NY.
34. Sebastiani P., Gussoni E., Kohane I.S., Ramoni M.F. (2003) Statistical challenges in functional genomics. Stat. Sci., 18, 33-70.
35. Shoukri M.M., McLachlan G.J. (1994) Parametric estimation in a genetic mixture model with application to nuclear family data. Biometrics, 50, 128-139.
36. Silverman B.W. (1986) Density Estimation for Statistics and Data Analysis. Chapman and Hall, London, UK.
37. Sorensen D., Gianola D. (2002) Likelihood, Bayesian and MCMC Methods in Quantitative Genetics. Springer, New York, NY.
38. Titterington D.M., Smith A.F.M., Makov U.E. (1985) Statistical Analysis of Finite Mixture Distributions. Wiley, Chichester, UK.
39. Verbeke G., Lesaffre E. (1996) A linear mixed effects model with heterogeneity in the random-effects population. J. Amer. Statist. Assoc., 91, 217-221.
40. Verbeke G., Mollenberghs G. (1997), Linear Mixed Models in Practice. Springer, New York, NY.
41. Westell R.A., Quaas R.L., Van Vleck L.D. (1988) Genetic groups in an animal model. J. Dairy Sci., 71, 1310-1318.