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All issues Volume 35 / No 2 (March-April 2003) Genet. Sel. Evol., 35 2 (2003) 137-158 References
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Issue
Genet. Sel. Evol.
Volume 35, Number 2, March-April 2003
Page(s) 137 - 158
DOI https://doi.org/10.1051/gse:2003001

References

  1. Antoniak C.E., Mixtures of Dirichlet processes with applications to nonparametric problems, Ann. Statist. 2 (1974) 1152-1174.
  2. Boldman K.G., Kriese L.A., Van Vleck L.D., Van Tassell C.P., Kachman S.D., A Manual for use of MTDFREML. A set of Programs to obtain Estimates of Variances and Covariances, U.S. Dept. of Agric., Agricultural Research Service, 1995.
  3. Bush C.A., MacEachern S.N., A semiparametric Bayesian model for randomized block designs, Biometrika 83 (1996) 275-285.
  4. Carlin B.P., Louis T.A., Bayes and Empirical Bayes Methods for Data Analysis, Chapman & Hall/CRC, New York, 2002.
  5. Doss H., Bayesian nonparametric estimation for incomplete data via successive substitution sampling, Ann. Stat. 22 (1994) 1763-1786.
  6. Escobar M.D., Estimating normal means with a Dirichlet process prior, Department of Statistics, Carnegie Mellon University, Technical Report No. 512, 1991.
  7. Escobar M.D., Estimating normal means with a Dirichlet process prior, J. Am. Stat. Assoc. 89 (1994) 268-275.
  8. Escobar M.D., West M., Bayesian density estimation and inference using mixtures, J. Am. Stat. Assoc. 90 (1995) 578-588.
  9. Ferguson T.S., A Bayesian analysis of some nonparametric problems, Ann. Stat. 1 (1973) 209-230.
  10. Gelfand A.E., Smith A.F.M., Gibbs sampling for marginal posterior expectations, Comm. Statist. Theory Methods 20 (1991) 1747-1766.
  11. Gelfand A.F., Smith A.F.M, Lee T.M., Bayesian analysis of constrained parameters and truncated data problems using Gibbs sampling, J. Am. Stat. Assoc. 87 (1992) 523-532.
  12. Gelman A., Rubin D.R., A single series from the Gibbs sampler provides a false sense of security, in: Bernardo J.M., Berger J., Dawid A.P., Smith A.F.M. (Eds.), Bayesian Statistics 4, Oxford University Press, Oxford, 1992, pp. 627-635.
  13. Ibrahim J.G., Kleinman K.P., Semiparametric Bayesian methods for random effects models, in: Dey D., Müller P., Sinha D. (Eds.), Practical Nonparametric and Semiparametric Bayesian Statistics, Lectures Notes in Statistics, 133, 2000, pp. 89-113.
  14. Kleinman K.P., Ibrahim J.G., A Semiparametric Bayesian approach to the random effects model, Biometrics 54 (1998) 921-938.
  15. Lui J., Nonparametric hierarchical Bayes via sequential imputation, Ann. Stat. 24 (1996) 911-930.
  16. MacEachern S.N., Estimation normal means with a conjugate style Dirichlet process prior, Commun. Stat. - Simula 23 (1994) 727-741.
  17. MacEachern S.N., Müller P., Estimating mixture of Dirichlet process models, J. Comput. Graph. Stat. 7 (1998) 223-238.
  18. Müller P., Erkani A., West M., Bayesian curve fitting using multivariate normal mixtures, Biometrika 83 (1996) 67-79.
  19. Strandén I., Gianola D., Attenuating effects of preferential treatment with student-t mixed linear models: a simulation study, Genet. Sel. Evol. 30 (1998) 565-583.
  20. Strandén I., Gianola D., Mixed effects linear models with t-distributions for quantitative genetic analysis: a Bayesian approach, Genet. Sel. Evol. 31 (1999) 25-42.
  21. Swart J.C., The origin and development of the Dormer sheep breed, Meat Ind. 31 May-June (1967).
  22. Thompson R., Sire evalution, Biometrics 35 (1979) 339-353.
  23. West M., Hyperparameter estimation in Dirichlet process mixture models, Duke University, ISDS, Technical Report 92 - A03, 1992.
  24. West M., Müller P., Escobar M.D., Hierarchical priors and mixture models, with applications in regression and density estimation, in: Smith A.F.M, Freeman P.R. (Eds.), Aspects of Uncertainty: a tribute to Lindley D.V., Wiley, London, 1994, pp. 363-386.
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