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References

Published online by Cambridge University Press:  05 November 2012

R. Mead
Affiliation:
University of Reading
S. G. Gilmour
Affiliation:
University of Southampton
A. Mead
Affiliation:
University of Warwick
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Chapter
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Statistical Principles for the Design of Experiments
Applications to Real Experiments
, pp. 565 - 567
Publisher: Cambridge University Press
Print publication year: 2012

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References

Bailey, R. A. (1982) The decomposition of treatment degrees of freedom in quantitative factorial experiments. Journal of the Royal Statistical Society, Series B, 44, 63–70.Google Scholar
Bartlett, M. S. (1978) Nearest neighbour models in the analysis of field experiments (with discussion). Journal of the Royal Statistical Society, Series B, 40, 147–174.Google Scholar
Besag, J. E. (1974) Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, Series B, 36, 192–236.Google Scholar
Besag, J. E. and Kempton, R. A. (1986) Analyis of field experiments using spatial statistics. Biometrics, 42, 231–251.CrossRefGoogle Scholar
Bleasdale, J. K. A. (1967) Systematic designs for spacing experiments. Experimental Agriculture, 3, 73–85.CrossRefGoogle Scholar
Box, G. E. P. and Draper, N. R. (1975) Robust designs. Biometrika, 62, 347–352.CrossRefGoogle Scholar
Box, G. E. P. and Tidwell, P. W. (1962) Transformation of the independent variables. Technometrics, 4, 531–550.CrossRefGoogle Scholar
Butler, N. A., Mead, R., Eskridge, K. M. and Gilmour, S. G. (2001) A general method of constructing E(s2)-optimal supersaturated designs. Journal of the Royal Statistical Society, Series B, 63, 621–632.Google Scholar
Bryan-Jones, J. and Finney, D. J. (1983) On an error in ‘Instruction to Authors’. Horticultural Science, 18, 279–282.Google Scholar
Carmer, S. G. and Jackobs, J. A. (1965) An exponential model for predicting optimum plant density and maximum corn yield. Agronomy Journal, 57, 241–244.CrossRefGoogle Scholar
Cleaver, T. J., Greenwood, D. J. and Wood, J. T. (1970) Systematically arranged fertiliser experiments. Journal of Horticultural Science, 45, 457–469.CrossRefGoogle Scholar
Cochran, W. G. and Cox, G. M. (1957) Experimental Designs, Second edition. New York: Wiley.Google Scholar
Cornell, J. A. (2002) Experiments with Mixtures, Third edition. New York: Wiley.CrossRefGoogle Scholar
Corsten, L. C. A. (1958) Vectors, a tool in statistical regression theory. Mededelingen van de Land-bouwhogeschool te Wageningen, 58, 1–92.Google Scholar
Cox, D. R. (1958) Planning of Experiments. New York: Wiley.Google Scholar
Cullis, B. R. and Gleeson, A. C. (1989) Efficiency of neighbour analysis for replicated variety trials in Australia. Journal of Agricultural Science, Cambridge, 113, 233–239.CrossRefGoogle Scholar
Cullis, B. R. and Gleeson, A. C. (1991) Spatial analysis of field experiments — an extension to two dimensions. Biometrics, 47, 1449–1460.CrossRefGoogle Scholar
Curnow, R. N. (1961) Optimal programmes for varietal selection. Journal of the Royal Statistical Society, Series B, 23, 282–318.Google Scholar
Darby, L. A. and Gilbert, N. (1958) The Trojan square. Euphytica, 7, 183–188.CrossRefGoogle Scholar
Davis, T. P. and Draper, N. R. (1995) A note on remnant three-level second order designs. Technical Report 954, Department of Statistics, University of Wisconsin-Madison.Google Scholar
Dorfman, R. (1943) The detection of defective members of large populations. Annals of Mathematical Statistics, 14, 436–440.CrossRefGoogle Scholar
Dyke, G. V. and Shelley, C. F. (1976) Serial designs balanced for effects of neighbours on both sides. Journal of Agricultural Science, Cambridge, 87, 303–305.CrossRefGoogle Scholar
Edmondson, R. N. (1994) Fractional factorial designs for factors with a prime number of quantitative levels. Journal of the Royal Statistical Society, Series B, 56, 611–622.Google Scholar
Edmondson, R. N. (1998) Trojan square and incomplete Trojan square designs for crop research. Journal of Agricultural Science, Cambridge, 131, 135–142.CrossRefGoogle Scholar
Eskridge, K. M., Gilmour, S. G., Mead, R., Butler, N. A. and Travnicek, D. A. (2004) Large supersaturated designs. Journal of Statistical Computation and Simulation, 74, 525–542.CrossRefGoogle Scholar
Finney, D. J. (1958) Statistical problems of plant selection. Bulletin of the International Statistical Institute, 36, 242–268.Google Scholar
Fisher, R. A. and Yates, F. (1963) Statistical Tables for Biological Agriculture and Medical Research. Edinburgh: Oliver & Boyd.Google Scholar
Francis, L. (1978) Experimental designs for the two-parameter exponential response curve. MSc dissertation, University of Reading.Google Scholar
Freeman, G. H. (1979) Some two-dimensional designs balanced for nearest neighbours. Journal of the Royal Statistical Society, Series B, 41, 88–95.Google Scholar
Gilmour, A., Cullis, B. and Verbyla, A. (1997) Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological and Environmental Statistics, 2, 269–293.CrossRefGoogle Scholar
Gilmour, S. G. (2006) Response surface designs for experiments in bioprocessing. Biometrics, 62, 323–331.CrossRefGoogle ScholarPubMed
Gilmour, S. G. (2006) Supersaturated designs in factor screening. In Screening (eds. S.M., Lewis and A. M., Dean), pp. 169–190. New York: Springer.Google Scholar
Gilmour, S. G. and Trinca, L. A. (2005) Fractional polynomial response surface models. Journal of Agricultural, Biological and Environmental Statistics, 10, 50–60.CrossRefGoogle Scholar
Gleeson, A. C. and Cullis, B. R. (1987) Residual maximum likelihood (REML) estimation of a neighbour model for field experiments. Biometrics, 43, 277–287.CrossRefGoogle Scholar
Gordon, T. and Foss, B. M. (1966) The role of stimulation in the delay of the onset of crying in the new-born infant. Journal of Experimental Psychology, 16, 79–81.Google Scholar
Green, P. J., Jennison, C. and Seheult, A. H. (1985) Analysis of field experiments by least squares smoothing. Journal of the Royal Statistical Society, Series B, 47, 299–315.Google Scholar
Hills, M. and Armitage, P. (1979) Two-period cross-over clinical trial. British Journal of Clinical Pharmacology, 8, 7–20.CrossRefGoogle ScholarPubMed
Hozumi, K., Asahira, T. and Kira, T. (1972) Intraspecific competition among higher plants: VI. Effect of some growth factors on the process of competition. Journal of the Institute of Polytechnics of Osaka City University, D7, 15–28.Google Scholar
John, J. A. and Williams, E. R. (1995) Cyclic and Computer Generated Designs, Second edition. London: Chapman & Hall.CrossRefGoogle Scholar
Kempthorne, O. (1952) The Design and Analysis of Experiments. New York: Wiley.Google Scholar
Kempton, R. A. and Howes, C. W. (1981) The use of neighbouring plot values in the analysis of variety trials. Applied Statistics, 30, 59–70.CrossRefGoogle Scholar
Kerr, M. K. and Churchill, G. A. (2001) Statistical design and the analysis of gene expression microarray data. Genetics Research, 77, 123–128.Google ScholarPubMed
Kuipers, N. H. (1952) Variantie-Analyse. Statistica, 6, 149–194.Google Scholar
Lee, Y., Nelder, J. A. and Pawitan, Y. (2006) Generalized Linear Models with Random Effects. London: CRC Press.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, Second edition. London: Chapman & Hall.CrossRefGoogle Scholar
Maindonald, J. H. and Cox, N. R. (1984) Use of statistical evidence in some recent issues of DSIR agricultural journals. New Zealand Journal of Agriculture, 27, 597–610.Google Scholar
Morse, P. M. and Thompson, B. K. (1981) Presentation of experimental results. Canadian Journal of Plant Science, 61, 799–802.Google Scholar
Nelder, J. A. (1962) New kinds of systematic design for spacing experiments. Biometrics, 18, 283–307.CrossRefGoogle Scholar
Nelder, J. A. (1966) Inverse polynomials, a useful group of multi-factor response functions. Biometrics, 22, 128–141.CrossRefGoogle Scholar
Nelder, J. A. (1991) Generalized linear models for enzyme kinetic data. Biometrics, 47, 1605–1615.CrossRefGoogle ScholarPubMed
Nelder, J. A. and Mead, R. (1965) A simplex method for function minimization. The Computer Journal, 7, 308–313.CrossRefGoogle Scholar
O'Neill, R. and Wetheril, G. B. (1971) The present state of multiple comparison methods (with discussion). Journal of the Royal Statistical Society, Series B, 33, 218–241.Google Scholar
Patterson, H. D. and Thompson, R. A. (1971) Recovery of inter-block information when block sizes are unequal. Biometrika, 5, 545–554.Google Scholar
Patterson, H. D. and Williams, E. R. (1976) A new class of resolvable incomplete block designs. Biometrika, 63, 83–92.CrossRefGoogle Scholar
Pearce, S. C. (1963) The use and classification of non-orthogonal designs (with discussion). Journal of the Royal Statistical Society, Series B, 25, 353–377.Google Scholar
Pearce, S. C. (1975) Row-and-column designs. Applied Statistics, 24, 60–74.Google Scholar
Pocock, S. J. (1979) Allocation of patients to treatment in clinical trials. Biometrics, 35, 183–197.CrossRefGoogle ScholarPubMed
Rayner, A. A. (1969) A First Course in Biometry for Agriculture Students. Pietermaritzburg: University of Natal Press.Google Scholar
Reid, D. (1972) The effects of long-term application of a wide range of nitrogen rates on the yields from perennial ryegrass swards with and without white clover. Journal of Agricultural Science, Cambridge, 79, 291–301.CrossRefGoogle Scholar
Rojas, B. A. (1963) The San Cristobal design for fertiliser experiments. Proceedings of the International Society of Sugar Came Technologists, Mauritius.Google Scholar
Rojas, B. A. (1972) The orthogonalised San Cristobal design. Proceedings of the International Society of Sugar Came Technologists, Louisiana.Google Scholar
Royston, P. and Altman, D. G. (1994) Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Applied Statistics, 43, 429–467.CrossRefGoogle Scholar
Ruppert, D., Cressie, N. and Carroll, R. (1989) A transformation/weighting model for estimating Michaelis— Menten parameters. Biometrics, 45, 637–656.CrossRefGoogle Scholar
Sobel, M. and Groll, P. A. (1959) Group testing to eliminate effectively all defectives in a binomial sample. Bell Systems Technology Journal, 38, 1179–1252.CrossRefGoogle Scholar
Sparrow, P. E. (1979) Nitrogen response curves of spring barley. Journal of Agricultural Science, Cambridge, 92, 307–317.CrossRefGoogle Scholar
Trinca, L. A. and Gilmour, S. G. (2000) An algorithm for arranging response surface designs in small blocks. Computational Statistics and Data Analysis, 33, 25–43. Erratum (2002), 40, 475.CrossRefGoogle Scholar
Trinca, L. A. and Gilmour, S. G. (2001) Multi-stratum response surface designs. Technometrics, 43, 25–33.CrossRefGoogle Scholar
Varnalis, A. I., Brennan, J. G., MacDougall, D. B. and Gilmour, S. G. (2004) Optimisation of high temperature puffing of potato cubes using response surface methodology. Journal of Food Engineering, 61, 153–163.CrossRefGoogle Scholar
Whitehead, J. R. (1997) Sequential Clinical Trials, Second edition. New York: Wiley.Google Scholar
Wilkinson, G. N. (1970) A general recursive procedure for analysis of variance. Biometrika, 57, 19–46.CrossRefGoogle Scholar
Wilkinson, G. N., Eckert, S. R., Hancock, T. W. and Mayo, O. (1983) Nearest neighbour (NN) analysis of field experiments (with discussion). Journal of the Royal Statistical Society, Series B, 45, 151–211.Google Scholar
Williams, R. M. (1952) Experimental designs for serially correlated observations. Biometrika, 49, 151–167.Google Scholar
Wit, E., Nobile, A. and Khanin, R. (2005) Near-optimal designs for dual channel microarray studies. Applied Statistics, 54, 817–830.Google Scholar
Yates, F. (1935) Complex experiments (with discussion). Supplement to the Journal of the Royal Statistical Society, 2, 181–247.CrossRefGoogle Scholar
Yates, F. (1936) A new method of arranging variety trials involving a large number of varieties. Journal of Agricultural Science, Cambridge, 26, 424–455.CrossRefGoogle Scholar

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  • References
  • R. Mead, University of Reading, S. G. Gilmour, University of Southampton, A. Mead, University of Warwick
  • Book: Statistical Principles for the Design of Experiments
  • Online publication: 05 November 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9781139020879.023
Available formats
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  • References
  • R. Mead, University of Reading, S. G. Gilmour, University of Southampton, A. Mead, University of Warwick
  • Book: Statistical Principles for the Design of Experiments
  • Online publication: 05 November 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9781139020879.023
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • R. Mead, University of Reading, S. G. Gilmour, University of Southampton, A. Mead, University of Warwick
  • Book: Statistical Principles for the Design of Experiments
  • Online publication: 05 November 2012
  • Chapter DOI: https://doi.org/10.1017/CBO9781139020879.023
Available formats
×