Advances in methodology for random regression analyses

Australian Journal of Experimental Agriculture

Volumn 45, Issue 7-8, 2005, Pages 847-858

  Meyer, K ^a  

^a UNIVERSITY OF NEW ENGLAND   (Australia)

Author keywords

B spline functions; Principal components; Reduced rank estimation

Indexed keywords

METHODOLOGY; REGRESSION ANALYSIS;

BOS TAURUS;

EID: 33645085623     PISSN: 08161089     EISSN: None     Source Type: Journal    
DOI: 10.1071/EA05040     Document Type: Conference Paper

References (38)

1. Burnham KP, Anderson DR (2004) Multimodel inference: Understanding {AIC} and {BIC} in model selection. Sociological Methods and Research 33, 261-304. doi:10.1177/0049124104268644
2. Crainiceau CM, Ruppert C, Carroll RJ (2004) Spatially adaptive Bayesian P-splines with heteroscedastic errors. Working paper no. 61, John Hopkins University, Department of Biostatistics. Available online at: www.bepress.com/jhubiostat/paper61 (verified 3 August 2005).
3. de Boor C (2001) 'A practical guide to splines.' 2nd edn. (Springer Verlag: New York)
4. Durbán M, Harezlak J, Wand MP, Carroll RJ (2005) Simple fitting of subject-specific curves for longitudinal data. Statistics in Medicine 24, 1153-1167. doi:10.1002/sim.1991
5. Eilers PHC (1999) Discussion on paper by Verbyla, Cullis, Kenward and Welham. Applied Statistics 48, 307-308.
6. Eilers PHC, Marx BD (1996) Flexible smoothing with B-splines and penalties (with discussion). Statistical Science 11, 89-121. doi:10.1214/ss/1038425655
7. Eilers PHC, Marx BD (2005) Splines, knots, and penalties. Available online at: http://www.stat.lsu.edu/faculty/bmarx/splines_knots_ penalties.pdf (verified 3 August 2005).
8. Foulley JL, Jaffrézic F, Robert-Granié C (2000) EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis. Genetics Selection Evolution 32, 129-141. doi:10.1051/gse:2000110
9. Gilmour AR, Thompson R, Cullis BR (1995) Average Information REML, an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51, 1440-1450.
10. Green PJ, Silverman BW (1994) 'Nonparametric regression and generalized linear models. A roughness penalty approach.' Monographs in statistics and applied probability. Vol. 58. (Chapman & Hall: London)
11. Harville DA (1997) 'Matrix algebra from a statistician's perspective.' (Springer Verlag)
12. Jaffrézic F, Pletcher SD (2000) Statistical models for estimating the genetic basis of repeated measures and other function-valued traits. Genetics 156, 913-922.
13. James GM, Hastie TJ, Sugar CA (2000) Principal component models for sparse functional data. Biometrika 87, 587-602. doi:10.1093/biomet/87.3.587
14. Jennrich PJ, Schluchter MD (1986) Unbalanced repeated-measures models with structured covariance matrices. Biometrics 42, 805-820.
15. Jollife IT (1986) 'Principal component analysis.' (Springer Verlag: New York)
16. Kirkpatrick M, Heckman N (1989) A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters. Journal of Mathematical Biology 27, 429-450. doi:10.1007/BF00290638
17. Kirkpatrick M, Lofsvold D, Bulmer M (1990) Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979-993.
18. Kirkpatrick M, Meyer K (2004) Simplified analysis of complex phenotypes: direct estimation of genetic principal components. Genetics 168, 2295-2306. doi:10.1534/genetics.104.029181
19. Meyer K (1998) Estimating covariance functions for longitudinal data using a random regression model. Genetics, Selection, Evolution 30, 221-240.
20. Meyer K (2000) Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livestock Production Science 65, 19-38. doi:10.1016/S0301-6226(99)00183-9
21. Meyer K (2001) Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects. Genetics, Selection, Evolution 33, 557-585. doi:10.1051/gse:2001102
22. Meyer K (2005a) Estimates of covariance functions for growth of Angus cattle from random regression analyses fitting B-spline functions. Proceedings of the Association for Advancement of Animal Breeding Genetics 16, 52-55.
23. Meyer K (2005b) Random regression analyses using B-splines to model growth of Australian Angus cattle. Genetics, Selection, Evolution 37, 473-500.
24. Meyer K (2005c) Reduced rank estimates of the genetic covariance matrix for live ultra-sound scan traits. Proceedings of the Association for Advancement of Animal Breeding Genetics 16, 56-59.
25. Meyer K (2005d) Sampling behaviour of reduced rank estimates of genetic covariance functions. Proceedings of the Association for Advancement of Animal Breeding Genetics 16, 286-289.
26. Meyer K, Carrick MJ, Donnelly BJP (1993) Genetic parameters for growth traits of Australian beef cattle from a multi-breed selection experiment. Journal of Animal Science 71, 2614-2622.
27. Meyer K, Kirkpatrick M (2005a) Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices. Genetics, Selection, Evolution. 37, 1-30. doi:10.1051/gse:20040304
28. Meyer K, Kirkpatrick M (2005b) Up hill, down dale: quantitative genetics of curvaceous traits. Philosophical Transactions of the Royal Society B 360, 1443-1455. doi:10.1098/rstb.2005.1681
29. Rice JA, Wu CO (2001) Nonparametric mixed effects models for unequally sampled noisy curves. Biometrics 57, 253-259. doi:10.1111/j.0006-341X.2001. 00253.x
30. Ruppert D, Carroll RJ (2000) Spatially-adaptive penalties for spline fitting. Australian and New Zealand Journal of Statistics 42, 205-223. doi:10.1111/1467-842X.00119
31. Ruppert D, Wand MP, Carroll RJ (2003) 'Semiparametric regression.' (Cambridge University Press: New York)
32. Shi M, Weiss RE, Taylor JMG (1996) An analysis of pediatric CD4 counts for acquired immune deficiency syndrome using flexible random curves. Applied Statistics 45, 151-164.
33. Smith AB, Cullis BR, Thompson R (2001) Analysing variety by environment data using multiplicative mixed models and adjustments for spatial field trends. Biometrics 57, 1138-1147. doi:10.1111/j.0006-341X.2001.01138.x
34. Thompson R, Cullis BR, Smith AB, Gilmour AR (2003) A sparse implementation of the Average Information algorithm for factor analytic and reduced rank variance models. Australian and New Zealand Journal of Statistics 45, 445-459. doi:10.1111/1467-842X.00297
35. Torres RAA, Quaas RL (2001) Determination of covariance functions for lactation traits on dairy cattle using random-coefficient regressions on B-splines. Journal of Animal Science 79(Suppl. 1), 112. [Abstract.]
36. Verbyla AR, Cullis BR, Kenward MG, Welham SJ (1999) The analysis of designed experiments and longitudinal data by using smoothing splines (with discussion). Applied Statistics 48, 269-311.
37. White IMS, Thompson R, Brotherstone S (1999) Genetic and environmental smoothing of lactation curves with cubic splines. Journal of Dairy Science 82, 632-638.
38. Wolfinger RD (1993) Covariance structure selection in general mixed models. Communications in Statistics - Simulation and Computing 22, 1079-1106.