The state of the art related to parameter correlation in two-parameter models has been reviewed in this paper. The apparent contradictions between the different authors regarding the ability of D--optimality to simultaneously reduce the correlation and the area of the confidence ellipse in two-parameter models were analyzed. Two main approaches were found: 1) those who consider that the optimality criteria simultaneously control the precision and correlation of the parameter estimators; and 2) those that consider a combination of criteria to achieve the same objective. An analytical criterion combining in its structure both the optimality of the precision of the estimators of the parameters and the reduction of the correlation between their estimators is provided. The criterion was tested both in a simple linear regression model, considering all possible design spaces, and in a non-linear model with strong correlation of the estimators of the parameters (Michaelis--Menten) to show its performance. This criterion showed a superior behavior to all the strategies and criteria to control at the same time the precision and the correlation.

翻译：本文综述了关于两参数模型中参数相关性的研究现状。分析了不同作者之间关于D-最优性在同时降低两参数模型相关性及置信椭圆面积能力上的明显矛盾。发现两种主要方法：1）认为优化准则能同时控制参数估计量的精度与相关性；2）认为需结合多种准则以实现相同目标。本文提出了一种分析性准则，其结构同时融合了参数估计量精度的最优性及估计量间相关性的降低。该准则在简单线性回归模型（考虑所有可能的设计空间）及参数估计量具有强相关性的非线性模型（Michaelis–Menten）中进行了测试，以展示其性能。结果表明，该准则在同时控制精度与相关性方面优于所有现有策略及准则。