Optimal experimental designs when an independent variable is potentially censored.

This paper considers the problem of constructing optimal approximate designs when an independent variable might be censored. The problem is which design should be applied in practice to obtain the best approximate design when a censoring distribution is assumed known in advance. The approach for finite or continuous design spaces deserves different attention. In both cases, equivalent theorems and algorithms are provided in order to calculate optimal designs. Some examples illustrate this approach for D-optimality. [ABSTRACT FROM AUTHOR]