References
- Bailey, R. A. (1985). “Nesting and Crossing in Design”. In Encyclopaedia of Statistical Sciences, Kotz, S. and Johnson, N. L., eds., vol. 6, pp. 181–185. New York, NY: Wiley.
- Bailey, R. A. (1996). “Orthogonal Partitions in Designs Experiments”. Designs, Codes and Cryptography 8, pp. 45–77.
- Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge, UK: Cambridge University Press.
- Bailey, R. A. and Monod, H. (2001). “Efficient Semi-Latin Rectangles: Designs for Plant Disease Experiments”. Scandinavian Journal of Statistics 28, pp. 257–270.
- Bate, S. T. and Chatfield, M. J. (2016). “Using the Structure of the Experimental Design and the Randomization to Construct a Mixed Model”. Journal of Quality Technology 48, pp. 365–387.
- Bate, S. T. and Clarke, R. A. (2014). The Design and Statistical Analysis of Animal Experiments. Cambridge, UK: Cambridge University Press.
- Borman, P. J.; Chatfield, M. J.; Damjanov, I.; and Jackson, P. (2011). “Method Ruggedness Studies Incorporating a Risk Based Approach: A Tutorial”. Analytica Chimica Acta 703, pp. 101–113.
- Brien, C. J. (1983). “Analysis of Variance Tables Based on Experimental Structure”. Biometrics 39, pp. 53–59.
- Brien, C. J. (1989). “A Model Comparison Approach to Linear Models”. Utilitas Mathematica 36, pp. 225–254.
- Brien, C. J. and Bailey, R. A. (2006). “Multiple Randomizations (with Discussion)”. Journal of the Royal Statistical Society, Series B 68, pp. 571–609.
- Brien, C. J. and Bailey, R. A. (2009). “Decomposition Tables for Experiments I. A Chain of Randomizations”. The Annals of Statistics 37, pp. 4184–4213.
- Brien, C. J. and Bailey, R. A. (2010). “Decomposition Tables for Experiments. II. Two-One Randomizations”. The Annals of Statistics 38, pp. 3164–3190.
- Brien, C. J. and Demétrio, C. C. B. (2009). “Formulating Mixed Models for Experiments Including Longditudinal Experiments”. Journal of Agricultural, Biological and Environmental Statistics 14 (3), pp. 253–280.
- Brien, C. J.; Harch, B. D.; Correll, R. L.; and Bailey, R. A. (2011). “Multiphase Experiments with at Least One Later Laboratory Phase. I. Orthogonal Designs”. Journal of Agricultural, Biological and Environmental Statistics 16, pp. 422–450.
- Finney, D. J. (1972). An Introduction to Statistical Science in Agriculture, 4th edition. Copenhagen, Munksgaard.
- Godolphin, J. D. (2004). “Simple Pilot Procedures for the Avoidance of Disconnected Experimental Designs”. Applied Statistics 53, pp. 133–147.
- Goos, P. and Donev, A. N. (2007). “Tailor-Made Split-Plot Designs for Mixture and Process Variables”. Journal of Quality Technology 39, 4, pp. 326–339.
- Goos, P. and Gilmour, S. G. (2012). “A General Strategy for Analyzing Data from Split-Plot and Multistratum Experimental Designs”. Technometrics 54 (4), pp. 340–354.
- Großmann, H. (2014). “Automating the Analysis of Variance of Orthogonal Designs”. Computational Statistics and Data Analysis 70, pp. 1–18.
- Hinkelmann, K. and Kempthorne, O. (2008). Design and Analysis of Experiments, vol 1. New York, NY: Wiley.
- Jones, B. J. and Nachtsheim, C. J. (2009). “Split-Plot Designs: What, Why and How”. Journal of Quality Technology 41 (4), 340–361.
- Lohr, S. L. (1995). “Hasse Diagrams in Statistical Consulting and Teaching”. The American Statistician 49, pp. 376–381.
- Montgomery, D. (2009). Design and Analysis of Experiments, 7th edition. New York, NY: Wiley.
- Preece, D. A. (2001). “Types of Factor in Experiments”. Journal of Statistical Planning and Inference 95 (1), pp. 269–282.
- R Core Team (2013). R: A language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.
- Smith, A. B.; Lim, P.; and Cullis, B. R. (2006). “The Design and Analysis of Multi-Phase Plant Breeding Experiments”. Journal of Agricultural Science 144 (5), pp. 393–409.
- Tjur, T. (1984). “Analysis of Variance Models in Orthogonal Designs”. International Statistical Review 52, pp. 33–65.
- Yates, F. (1936). “A New Method of Arranging Variety Trials Involving a Large Number of Varieties”. Journal of Agricultural Science 26, pp. 424–455.