. We advocate a numerically reliable and accurate approach for practical parameter 5 identifiability analysis: Applying column subset selection (CSS) to the sensitivity matrix, instead of 6 computing an eigenvalue decomposition of the Fischer information matrix. Identifiability analysis 7 via CSS has three advantages: (i) It quantifies reliability of the subsets of parameters selected as 8 identifiable and unidentifiable. (ii) It establishes criteria for comparing the accuracy of different 9 algorithms. (iii) The implementations are numerically more accurate and reliable than eigenvalue 10 methods applied to the Fischer matrix, yet without an increase in computational cost. The effective- 11 ness of the CSS methods is illustrated with extensive numerical experiments on sensitivity matrices 12 from six physical models, as well as on adversarial synthetic matrices. Among the CSS methods, 13 we recommend an implementation based on the strong rank-revealing QR algorithm because of its 14 rigorous accuracy guarantees for both identifiable and non-identifiable parameters. 15
分享
引用
