Model-based design of experiments (MBDoE) has been widely used for efficient development of mathematical models, which can then be used for various applications for real world systems. The conventional optimality criteria for MBDoE can suffer from ill-conditioning of design matrix, which can be easily encountered in practical systems. To alleviate this problem, in this work, an alternative optimality criterion is proposed, whose formulation depends on mean squared error of biased estimators obtained by parameter subset selection. Such formulation is applied to subset selection methods by ranking and by transformation. Then, using an illustrative linear example, the performance of the proposed criterion is compared with three conventional criteria: A-, D-, and E-optimality criteria. Through the case study, it is shown that the proposed criterion can outperform the conventional ones in all the cases, generating linear models with smaller prediction errors, and it can provide better results with subset selection by transformation.

基于模型的实验设计 (MBDoE) 已广泛用于数学模型的高效开发，然后可用于现实世界系统的各种应用。MBDoE 的传统最优性标准可能会受到设计矩阵病态的影响，这在实际系统中很容易遇到。为了缓解这个问题，在这项工作中，提出了一种替代的最优性标准，其公式取决于通过参数子集选择获得的有偏估计量的均方误差。这种公式通过排序和转换应用于子集选择方法。然后，使用一个说明性的线性示例，将建议标准的性能与三个传统标准进行比较：A-、D-和 E-最优标准。通过案例研究，