A review of some exchange algorithms for constructing discrete D-optimal designs
Computational Statistics & Data Analysis - Second special issue on optimization techniques in statistics
The specification of rank reducing observation sets in experimental design
Computational Statistics & Data Analysis
Choosing cross-over designs when few subjects are available
Computational Statistics & Data Analysis
Editorial: Special issue on algorithms for design of experiments
Computational Statistics & Data Analysis
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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.