Abstract
Linear parametric functions affected by deleting or augmenting sets of design points are identified explicitly using principal components of the predictive dispersion at those points, offering fresh insight and significant computational savings. Leverages from regression diagnostics are seen to determine efficiencies due to augmenting or deleting single points. Eight small second-order designs are studied in detail with supporting numerical displays. Comparisons are drawn to other approaches from the literature.
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