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All issues Volume 37 / No 5 (September-October 2005) Genet. Sel. Evol., 37 5 (2005) 473-500 References
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Issue
Genet. Sel. Evol.
Volume 37, Number 5, September-October 2005
Page(s) 473 - 500
DOI https://doi.org/10.1051/gse:2005012
References of  Genet. Sel. Evol. 37 (2005) 473-500
  1. Cantet R.J.C., B-spline fitting of time as an alternative to contemporary group effects. CD-ROM Seventh World Congr. Genet. Appl. Livest. Prod. (2002), Communication No. 20-11.
  2. de Boor C., A Practical Guide to Splines, vol. 27 of Springer Series in Applied Mathematics, Springer Verlag, New York, 2nd edn., 2001.
  3. De Groot B.J., Keown J.F., Kachman S.D., Van Vleck L.D., Estimates of genetic parameters and lactation curves with a cubic spline model for Holstein cows treated with bovine somatotropin, J. Anim. Sci. 81 Suppl. 1 (2003) 37.
  4. Dierkx P., Curve and Surface Fitting with Splines, Oxford University Press, Oxford and New York, 1993.
  5. Dodge Y., The Oxford Dictionary of Statistical Terms, Oxford University Press, Oxford and New York, 2003.
  6. Durbán M., Harezlak J., Wand M.P., Carroll R.J., Simple fitting of subject-specific curves for longitudinal data, Stat. Med. 24 (2005) 1153-1167 [CrossRef] [PubMed] [MathSciNet].
  7. Eilers P.H.C., Discussion on paper by Verbyla, Cullis, Kenward and Welham, Appl. Stat. 48 (1999) 307-308.
  8. Eilers P.H.C., Marx B.D., Flexible smoothing with B-splines and penalties (with discussion), Stat. Sci. 11 (1996) 89-121 [MathSciNet].
  9. Eilers P.H.C., Marx B.D., Splines, knots, and penalties, J. Comput. Graph. Statist. (2005), in press. http://www.stat.lsu.edu/faculty/marx/splines_knots_penalties.pdf.
  10. El Faro L., Albuquerque L.G., Fries L.A., Adjusting lactation curves with segmented polynomials, in: Proceedings Sixth World Congr. Genet. Appl. Livest. Prod., Vol. 23, Armidale, Australia (1998), pp. 411-414.
  11. Fuller W.A., Grafted polynomials as approximating functions, Austr. J. Agric. Econ. 13 (1969) 35-46.
  12. Gilmour A., Gogel B., Cullis B.R., Welham S.J., Thompson R., ASREML User Guide Release 1.0. VSN International Ltd., Hemel Hempstead, HP1 1ES, UK (2001).
  13. Green P.J., Silverman B.W., Nonparametric regression and Generalized Linear Models. A Roughness Penalty Approach. Monographs in Statistics and Applied Probability 58, Chapman & Hall, London, 1994.
  14. Guo W., Functional mixed effects models, Biometrics 58 (2002) 121-128 [CrossRef] [PubMed] [MathSciNet].
  15. Huisman A.E., Veerkamp R.F., van Arendonk J.A.M., Genetic parameters for various random regression models to describe the weight data of pigs, J. Anim. Sci. 80 (2002) 575-582 [PubMed].
  16. Ihaka R., Gentleman R., R: A language for data analysis and graphics, J. Comput. Graph. Statist. 5 (1996) 299-314.
  17. James G.M., Hastie T.J., Sugar C.A., Principal component models for sparse functional data, Biometrika 87 (2000) 587-602 [CrossRef] [MathSciNet].
  18. Jamrozik J., Schaeffer L.R., Bayesian comparison of random regression models for test-day yields in dairy cattle, CD-ROM Seventh World Congr. Genet. Appl. Livest. Prod. (2002), Communication No. 1-03.
  19. Jollife I.T., Principal Component Analysis, Springer Series in Statistics, Springer Verlag, New York, 1986.
  20. Kirkpatrick M., Heckman N., A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters, J. Math. Biol. 27 (1989) 429-450 [CrossRef] [PubMed] [MathSciNet].
  21. Kirkpatrick M., Meyer K., Simplified analysis of complex phenotypes: Direct estimation of genetic principal components, Genetics 168 (2004) 2295-2306 [CrossRef] [PubMed].
  22. Kirkpatrick M., Lofsvold D., Bulmer M., Analysis of the inheritance, selection and evolution of growth trajectories, Genetics 124 (1990) 979-993 [PubMed].
  23. Koots K.R., Gibson J.P., Smith C., Wilton J.W., Analyses of published genetic parameter estimates for beef production traits. 1. Heritability, Anim. Breed. Abstr. 62 (1994) 309-337.
  24. Meyer K., Estimates of genetic and phenotypic covariance functions for postweaning growth and mature weight of beef cows, J. Anim. Breed. Genet. 116 (1999) 181-205 [CrossRef].
  25. Meyer K., Random regressions to model phenotypic variation in monthly weights of Australian beef cows, Livest. Prod. Sci. 65 (2000) 19-38 [CrossRef].
  26. Meyer K., Estimates of covariance functions for growth of Australian beef cattle from a large set of field data. CD-ROM Seventh World Congr. Genet. Appl. Livest. Prod. (2002), Communication No. 11-01.
  27. Meyer K., "RRGIBBS" - A program for simple random regression analyses via Gibbs sampling. CD-ROM Seventh World Congr. Genet. Appl. Livest. Prod. (2002), Communication No. 28-27.
  28. Meyer K., Estimates of genetic covariance functions for growth of Australian Angus cattle, J. Anim. Breed. Genet. 122 (2005) 73-85 [CrossRef].
  29. Meyer K., Sampling behaviour of reduced rank estimates of genetic covariance functions, Proc. Ass. Advan. Anim. Breed. Genet. 16 (2005) 226-289.
  30. Meyer K., Kirkpatrick M., Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices, Genet. Sel. Evol. 37 (2005) 1-30 [EDP Sciences] [CrossRef] [PubMed].
  31. Meyer K., Kirkpatrick M., Up hill, down dale: quantitative genetics of curvaceous traits, Phil. Trans. R. Soc. B 360 (2005) 1443-1455.
  32. Nobre P.C.R., Misztal I., Tsuruta S., Bertrand J.K., Silva L.O.C., Lopes P.S., Analyses of growth curves of Nellore cattle by multiple-trait and random regression models, J. Anim. Sci. 81 (2003) 918-926 [PubMed].
  33. Rice J.A., Wu C.O., Nonparametric mixed effects models for unequally sampled noisy curves, Biometrics 57 (2001) 253-259 [CrossRef] [PubMed] [MathSciNet].
  34. Ruppert D., Wand M.P., Carroll R.J., Semiparametric Regression, Cambridge University Press, New York, 2003.
  35. Schaeffer L.R., Application of random regression models in animal breeding, Livest. Prod. Sci. 86 (2004) 35-45 [CrossRef].
  36. Shi M., Weiss R.E., Taylor J.M.G., An analysis of pediatric CD4 counts for acquired immune deficiency syndrome using flexible random curves, Appl. Stat. 45 (1996) 151-164.
  37. Strandberg E., Grandinson E., Adjusting for seasonal effects in an animal model using fuzzy classification, Proceedings Sixth World Congr., Genet. Appl. Livest. Prod. 25 (1998) 633-636.
  38. Torres R.A.A., Markov chain Monte Carlo methods for estimating the covariance structure of longitudinal data - an application to dairy cattle data, Ph.D. thesis, Cornell University, 2001.
  39. Torres R.A.A., Quaas R.L., Determination of covariance functions for lactation traits on dairy cattle using random-coefficient regressions on B-splines, J. Anim. Sci. 79 Suppl. 1 (2001) 112.
  40. Verbyla A.R., Cullis B.R., Kenward M.G., Welham S.J., The analysis of designed experiments and longitudinal data by using smoothing splines (with discussion), Appl. Stat. 48 (1999) 269-311.
  41. White I.M.S., Thompson R., Brotherstone S., Genetic and environmental smoothing of lactation curves with cubic splines, J. Dairy Sci. 82 (1999) 632-638 [PubMed].