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An efficient procedure for the avoidance of disconnected incomplete block designs

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  • Godolphin, J.D.
  • Warren, H.R.

Abstract

Knowledge of the cardinality and the number of minimal rank reducing observation sets in experimental design is important information which makes a useful contribution to the statistician’s tool-kit to assist in the selection of incomplete block designs. Its prime function is to guard against choosing a design that is likely to be altered to a disconnected eventual design if observations are lost during the course of the experiment. A method is given for identifying these observation sets based on the concept of treatment separation, which is a natural approach to the problem and provides a vastly more efficient computational procedure than a standard search routine for rank reducing observation sets. The properties of the method are derived and the procedure is illustrated by four applications which have been discussed previously in the literature.

Suggested Citation

  • Godolphin, J.D. & Warren, H.R., 2014. "An efficient procedure for the avoidance of disconnected incomplete block designs," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1134-1146.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:1134-1146
    DOI: 10.1016/j.csda.2013.09.025
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    References listed on IDEAS

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    1. Bate, S.T. & Godolphin, E.J. & Godolphin, J.D., 2008. "Choosing cross-over designs when few subjects are available," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1572-1586, January.
    2. R. A. Bailey, 2007. "Designs for two‐colour microarray experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(4), pages 365-394, August.
    3. J. D. Godolphin, 2004. "Simple pilot procedures for the avoidance of disconnected experimental designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 133-147, January.
    4. Nguyen, Nam-Ky & Miller, Alan J., 1992. "A review of some exchange algorithms for constructing discrete D-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 14(4), pages 489-498, November.
    5. Godolphin, J.D., 2006. "The specification of rank reducing observation sets in experimental design," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1862-1874, December.
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