Some notions of optimality in experimental design.
Figure 1: Three candidate designs explored in our K=1 simulation.
Figure 2: The original regressions overlaid (top) and a histogram of all the slopes (bottom).
A design is D-optimal if |Cov(ˆβ)| is minimized. In our picture, this happens when the width of the histogram of ˆβ is minimized. The determinant generalizes the notion of width to higher-dimensions (specifically, it gives the volume of a high-dimensional parallelogram.)
A design is G-optimal if max is minimized. In our picture, this happens when the maximum vertical spread of the prediction band is minimized.
A design is V-optimal if \int_{\left[-1, 1\right]^{K}}. \text{Var}\left(\hat{y}\left(x\right)\right)dx is minimized. In our picture, this happens when the area of the prediction band is minimized
Figure 3: Summary of alternative optimality definitions.
Figure 4: Three candidate designs when studying 2 factors, all using 4 samples. The 2^2 design is at the bottom.