Model selection and its consequences for different split-plot designs with spatial covariance and trend

Plant Breeding

Volumn 129, Issue 6, 2010, Pages 590-598

  Spilke, J ^a   Richter, C ^b   Piepho, H P ^c  

^a MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

^b HUMBOLDT UNIVERSITY   (Germany)

^c UNIVERSITY OF HOHENHEIM   (Germany)

Author keywords

Kenward Roger method; Mean squared error; Model selection; Power; Satterthwaite method; Spatial covariance; Spatial trend; Split plot design; Type I error rate

Indexed keywords

EID: 78649574276     PISSN: 01799541     EISSN: 14390523     Source Type: Journal    
DOI: 10.1111/j.1439-0523.2010.01795.x     Document Type: Article

References (28)

1. Akaike, H., 1969: Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21, 243-247.
2. Burnham, K. P., and D. R. Anderson, 2002: Model Selection and Multimodel Inference, 2nd edn. Springer, New York.
3. Campbell, B. T., and P. J. Bauer, 2007: Improving the precision of cotton performance trials conducted on highly variable soils of the Southeastern USA Coastal Plain. Plant Breed. 126, 622-627.
4. Fai, A. H. T., and P. L. Cornelius, 1996: Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments. J. Statist. Comput. Simul. 54, 363-378.
5. Giesbrecht, F. G., and J. C. Burns, 1985: Two-stage analysis based on a mixed model: large-sample asymptotic theory and small-sample simulation results. Biometrics 41, 477-486.
6. Hu, X., J. Spilke, and C. Richter, 2006: The influence of spatial covariance on the type I error and the power for different evaluation models. Biome. Lett. 43, 19-37.
7. Hurvich, C. M., and C. L. Tsai, 1989: Regression and time series model selection in small samples. Biometrika 76, 297-397.
8. John, J. A., and E. R. Williams, 1995: Cyclic and Computer Generated Designs, 2nd edn. Chapman and Hall, London.
9. Kackar, A. N., and D. A. Harville, 1984: Approximation for standard errors of estimators of fixed and random effects in mixed linear models. J. Am. Stat. Assoc. 79, 853-861.
10. Kenward, M. G., and J. H. Roger, 1997: Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983-997.
11. Kenward, M. G., and J. H. Roger, 2009: An improved approximation to the precision of fixed effects from restricted maximum likelihood. Comput. Stat. Data Anal. 53, 2583-2595.
12. Lee, H., and S. K. Ghosh, 2009: Performance of information criteria for spatial models. J. Stat. Comput Simul. 79, 93-106.
13. Müller, B. U., K. Kleinknecht, J. Möhring, and H. P. Piepho, 2010: Efficiency and precision of nearest neighbour analyses of sugar beet and barley trials. Crop Sci 50, 794-802.
14. Patterson, H. D., and E. R. Williams, 1976: A new class of resolvable incomplete block designs. Biometrika 63, 39-47.
15. Piepho, H. P., and E. R. Williams, 2010: Linear variance models for plant breeding trials. Plant Breed. 129, 1-8.
16. Piepho, H. P., C. Richter, and E. R. Williams, 2008: Nearest neighbour adjustment and linear variance models in plant breeding trials. Biom. J. 50, 164-189.
17. Sachs, L., and J. Hedderich, 2009: Angewandte Statistik, 13rd edn. Springer, Heidelberg.
18. Satterthwaite, F. E., 1941: Synthesis of variance. Psychometrika 6, 309-316.
19. Schabenberger, O., and F. J. Pierce, 2002: Contemporary Statistical Models for the Plant and Soil Sciences. CRC Press, Boca Raton.
20. Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat. 6, 461-464.
21. Spilke, J., and C. Richter, 2007: Comparison of the effectiveness of model selection methods in the presence of spatial covariance. In: H. Bleiholder, and H.-P. Piepho (ed.), Agricultural Field Trials - Today and Tomorrow. Proceedings of the International Symposium 08-10 October 2007, Stuttgart-Hohenheim, Germany, 221-226.
22. Spilke, J., H. P. Piepho, and U. Meyer, 2004: Approximating the degrees of freedom for contrasts of genotype laid out as subplots in an alpha-design in a split-plot experiment. Plant Breed. 123, 193-197.
23. Spilke, J., H. P. Piepho, and X. Hu, 2005: A simulation study on tests of hypotheses and confidence intervals for fixed effects in mixed models for blocked experiments with missing data. J. Agric. Biol. Environ. Stat. 10, 374-389.
24. Stroup, W. W., 2002: Power analysis based on spatial effects mixed models: a tool for comparing design and analysis strategies in the presence of spatial variability. J. Agric. Biol. Environ. Stat. 7, 491-511.
25. Stroup, W. W., P. S. Baenziger, and D. K. Mulitze, 1994: Removing spatial variation from wheat yield trials: a comparison of methods. Crop Sci. 86, 62-66.
26. Williams, E. R., J. A. John, and D. Whitaker, 2006: Construction of resolvable row-column designs. Biometrics 62, 103-108.
27. Wolfinger, R. D., 1993: Covariance structure selection in general mixed models. Commun. Stat. A 22, 1079-1106.
28. Zimmerman, D. L., and D. A. Harville, 1991: A random field approach to the analysis of field-plot experiments and other spatial experiments. Biometrics 47, 223-239.