论文标题
基于敏感性矩阵的反问题制定方法
A Sensitivity Matrix Based Methodology for Inverse Problem Formulation
论文作者
论文摘要
我们提出了一种算法来选择参数子集组合，可以使用给定数据集使用普通最小二乘（OLS）逆问题制定估计。首先，该算法选择对应于具有全等级的灵敏度矩阵的参数组合。其次，该算法涉及使用Fisher Information矩阵倒数的不确定性定量。参数的名义值用于构建合成数据集，并探索从使用OLS程序估算的参数中删除某些参数的效果。我们将这些效果量化为分数的分数，该效果使用标准误差的范围定义的矢量参数，以将估计值组成的组件除以估计值。在某些情况下，该方法导致参数的标准误差降低至估计值的1 \％。
We propose an algorithm to select parameter subset combinations that can be estimated using an ordinary least-squares (OLS) inverse problem formulation with a given data set. First, the algorithm selects the parameter combinations that correspond to sensitivity matrices with full rank. Second, the algorithm involves uncertainty quantification by using the inverse of the Fisher Information Matrix. Nominal values of parameters are used to construct synthetic data sets, and explore the effects of removing certain parameters from those to be estimated using OLS procedures. We quantify these effects in a score for a vector parameter defined using the norm of the vector of standard errors for components of estimates divided by the estimates. In some cases the method leads to reduction of the standard error for a parameter to less than 1\% of the estimate.
