In the context of experimental design, achieving accurate and robust predictions across a range of conditions is crucial. Traditional optimality criteria like D-optimality focus on minimizing the determinant of the covariance matrix of the parameter estimates, which is useful for precise estimation of model parameters. However, when the primary goal is to ensure that predictions made by the model are reliable across the entire design space, G-optimality becomes the criterion of choice. G-optimality aims to minimize the maximum prediction variance over the design space, thereby ensuring the worst-case prediction variance is as low as possible. The current study does a comparison of various second order rotatable designs (SORDS) constructed using trigonometric functions on their G-optimality criteria.

optimality criteria, optimal design, prediction variance