\(J_{A}=trace\left(\left(\mathbf{\text{FIM}}\right)^{-1}\right)\) |
A-optimal design minimizes total parameter variance. |
(1.1) |
\(J_{D}=det\left(\left(\mathbf{\text{FIM}}\right)^{-1}\right)\) |
D-optimal design minimizes the volume of the joint confidence
interval for the parameters. |
(1.2) |
\(J_{E}=\lambda_{\max}(\mathbf{\text{FIM}}^{-1})\) |
E-optimal design minimizes the largest eigenvalue of the
FIM, thereby minimizing the uncertainties in the worst-case
direction in the parameter space. |
(1.3) |
\(J_{G}=max\ \left(\text{diag}\left(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\right)\right)\) |
G-optimal design minimizes the maximum variance of model
predictions at user-specified operating conditions of interest,
specified using a matrix\(\ \mathbf{W}.\) This is equivalent to
minimizing the largest value of the diagonal elements of
\(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\).
|
(1.4) |
\(J_{V}=trace\left(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\right)\) |
V-optimal design minimizes the total variance of model
predictions at user-specified operating conditions of interest, which
are specified using matrix\(\ \mathbf{W}.\) This is equivalent to
minimizing the trace of
\(\mathbf{W}\left(\mathbf{\text{FIM}}\right)^{-1}\mathbf{W}^{\mathbf{T}}\). |
(1.5) |