Assessment of homogeneity vs. heterogeneity of residual variance in random regression test-day models in a bayesian analysis

Journal of Dairy Science

Volumn 86, Issue 10, 2003, Pages 3374-3385

  Lopez Romero P ^a   Rekaya, R ^b   Carabano M J ^a  

^a INIA   (Spain)

^b UNIVERSITY OF GEORGIA   (United States)

Author keywords

Bayesian analysis; Heterogeneity of residual variance; Random regression test day models

Indexed keywords

ANALYSIS OF VARIANCE; ANIMAL; ARTICLE; BAYES THEOREM; CATTLE; COMPARATIVE STUDY; ENVIRONMENT; FEMALE; GENETICS; LACTATION; MATHEMATICS; PHYSIOLOGY; REGRESSION ANALYSIS; STATISTICAL MODEL;

ANALYSIS OF VARIANCE; ANIMALS; BAYES THEOREM; CATTLE; ENVIRONMENT; FEMALE; LACTATION; MATHEMATICS; MODELS, STATISTICAL; REGRESSION ANALYSIS;

EID: 3242875751     PISSN: 00220302     EISSN: None     Source Type: Journal    
DOI: 10.3168/jds.S0022-0302(03)73941-1     Document Type: Article

References (35)

1. Ali, T. E., and L. R. Schaeffer. 1987. Accounting for covariances among test day milk yield in dairy cows. Can. J. Anim. Sci. 67:637-645.
2. Brotherstone, S., I. M. S. White, and K. Meyer. 2000. Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves. Anim. Sci. 70:407-415.
3. Chib, S. 1995. Marginal likelihood from the Gibss output. J. Am. Statist. Ass. 90:1313-1321.
4. Gamerman, D. 1997. Markov Chain Monte Carlo. Chapman and Hall, New York, NY.
5. García-Cortés, L. A., M. Rico, and E. Groeneveld. 1998. Using coupling with the Gibbs sampler to assess convergence in animal models. J. Anim. Sci. 76:441-447.
6. Gelfand, A. E. 1996. Model determination using sampling-based methods. Pages 145-161 in Markov Chain Montecarlo in Practice. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter eds. Chapman and Hall, New York, NY.
7. Gelfand, A. E., D. K. Dey, and H. Chang. 1992. Model determination using predictive distributions with implementation via sampling-based methods. Pages 147-167 in Bayesian Statistics 4. J. M. Bernardo, J. O. Berger, A. P. David, and A. F. M. Smith, eds. Oxford University Press, Oxford, UK.
8. Gelfand, A. E., and D. K. Dey. 1994. Bayesian model choice: Asymptotic and exact calculations. J. R. Statist. Soc. B. 56:501-514.
9. Gelman, A., and D. B. Rubin. 1992. A single series from the Gibbs sampler provides a false sense of security. Pages 625-632 in J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, eds. Bayesian Statistics IV. Oxford University Press, Oxford, U.K.
10. Gelman, A., B. J. Carlin, H. S. Stern, and D. B. Rubin. 1995. Bayesian data analysis. Chapman and Hall, New York, NY.
11. Hernández, D., P. López-Romero, M. J. Carabaño, and R. Alenda. 1999. Aplicación de la metodología Bayesiana en la estima de parámetros genéticos para la producción de leche. VII Jornadas de Producción Animal. 11-13 Mayo, 1999, Zaragoza (Spain). ITEA Vol. Extra 20:318-320.
12. Jaffrezik, F., I. M. S. White, R. Thompson, and W. G. Hill. 2000. A link function approach to model heterogeneity of residual variances over time in lactation curve analysis. J. Dairy Sci. 83:1089-1093.
13. Jamrozik, J., and L. R. Schaeffer. 1997. Estimates of genetic parameters for a test day model with random regressions for yield traits of first lactation Holsteins. J. Dairy Sci. 80:762-770.
14. Jamrozik, J., L. R. Schaeffer, and J. C. M. Dekkers. 1996. Random regression models for production traits in Canadian Holsteins. Interbul Bull. No. 14:124-134.
15. Jamrozik, J., J. Kistemaker, L. R. Schaeffer, and J. C. M. Dekkers. 1997. Comparison of possible covariates for use in a random regression model for analyses of test-day yields. J. Dairy Sci 80:2550-2556.
16. Johnson, V. E. 1996. Studying convergence of Markov chain Monte Carlo algorithms using coupled sample paths. J. Am. Statist. Assoc. 91:154-166.
17. Kass, R. E., and A. E. Raftery. 1995. Bayes factors. J. Am. Statist. Assoc. 90:773-795.
18. Kirkpatrick, M., D. Lofsvold, and M. Bulmer. 1990. Analysis of the inheritance, selection, and evolution of growth trajectories. Genetics 124:979-993.
19. López-Romero, P., and M. J. Carabaño. 2003. Evaluating alternative random regression models to analyse first lactation daily milk yield data in Holstein-Friesian cattle. Livest. Prod. Sci. 82:81-96.
20. Misztal, I., T. Strabel, J. Jamrozik, E. A. Mäntysaari, and T. H. E. Meuwissen. 2000. Strategies for estimating the parameters needed for different test-day models. J. Dairy Sci. 83:1125-1134.
21. Newton, M. A., and A. E. Raftery. 1994. Approximate Bayesian Inference with the weighted likelihood bootstrap. J. R. Statist. Soc. B. 56:3-48.
22. Olori, V. E., W. G. Hill, B.J . McGuirk, and S. Brotherstone. 1999a. Estimating variance components for test day milk records by restricted maximum likelihood with a random regression model. Livest. Prod. Sci. 61:53-63.
23. Olori, V. E., W. G. Hill, and S. Brotherstone. 1999b. The structure of the residual error variance of test day milk yield in random regression models. Interbull Bull. 20:103-108.
24. Pool, M. H., L. L. G. Janss, and T. H.E . Meuwissen. 2000. Genetic parameters of Legendre polynomials for first parity lactation curves. J. Dairy Sci. 83:2640-2649.
25. Raftery, A. E. 1996. Hypothesis testing and model selection. Pages 145-161 in Markov Chain Montecarlo in Practice. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, eds. Chapman and Hall, New York.
26. Raftery, A. E., and S. M. Lewis. 1992. How many iterations in the Gibbs sampler. Pages 763-773 in Bayesian Statistics 4. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, eds. Oxford University Press, Oxford, UK.
27. Raftery, A., and S. M. Lewis. 1995. Gibbsit Version 2.0. http://lib.stat.-cmu.edu/general/gibbsit.
28. Rekaya, R., M. J. Carabaño, and M. A. Toro. 1999. Use of test day yields for the genetic evaluation of production traits in Holstein-Friesian cattle. Livest. Prod. Sci. 57:203-217.
29. Rekaya, R., M. J. Carabaño, and M. A. Toro. 2000. Assessment of heterogeneity of residual variances using changepoint techniques. Genet. Sel. Evol. 32:383-394.
30. Schnyder, U., A. Hofer, F. Labroue, and N. Künzi. 2001. Genetic parameters of random regression model for daily feed intake of performance tested French Landrace and Large White growing pigs. Genet. Sel. Evol. 33:635-658.
31. Sorensen, D. 1996. Gibbs sampling in quantitative genetics. Internal report no. 82 from the Danish Institute of Animal Sciencem, Foulum, Denmark.
32. Stephens, D. A. 1994. Bayesian retrospective multiple-changepoint identification. Appl. Statist. 43:159-178.
33. Van der Werf, J. H. J., M. E. Goddard, and K. Meyer. 1998. The use of covariance function and random regressions for genetic evaluation of milk production based on test day records. J. Dairy Sci. 81:3300-3308.
34. Varona, L., C. Moreno, L. A. García-Cortés, and J. Altarriba. 1998. Bayesian análisis of wood's curve for Spanish dairy cows. J. Dairy Sci. 81:1469-1478.
35. Wood, P. D. P. 1967. Algebraic model of the lactation curve in cattle. Nature 216:164-165.