Compromise designs under baseline parameterization

- The paper studies a class of compromise designs from adding runs to one-factor-at-a-time designs.
- A theoretical result is established for the case of adding one run.
- A complete search algorithm is developed to find optimal designs for adding two or more runs.

We consider estimation of main effects using two-level fractional factorial designs under the baseline parameterization. Previous work in the area indicates that orthogonal arrays are more efficient than one-factor-at-a-time designs whereas the latter are better than the former in terms of minimizing the bias due to non-negligible interactions. Using efficiency criteria, this paper examines a class of compromise designs obtained by adding runs to one-factor-at-a-time designs. A theoretical result is established for the case of adding one run. For adding two or more runs, we develop a complete search algorithm to find optimal compromise designs.