Standard errors and confidence intervals in inverse problems: sensitivity and associated pitfalls
By H. Banks, Stacey L. Ernstberger, Sarah L. Grove. 2007
66 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Mathematical Physics
View Paper: https://www.semanticscholar.org/paper/0219f5ac01ac79c3857b5586364d9f8d27b72945
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In Computational Theory and Mathematics
In Control and Systems Engineering
Citations in the corpus
- An Inverse Problem Statistical Methodology Summary (2009, background): “Alternatively, if the f(tj, ~ θ) correspond to longitudinal observations ~y(tj) = C~x(tj; ~ θ) of solutions ~x ∈ R to a parameterized N -vector differential equation system ~̇x = ~g(t, ~x(t), ~ θ) as in (1), then one can use the N × p matrix sensitivity equations (see [4, 9] and the references there”
- Generalized sensitivities and optimal experimental design (2010, unknown): “This example is widely used in the research literature, and has an analytical solution, few parameters, and well-known dynamics (see [8, 10, 27]).”
- Sensitivity functions and their uses in inverse problems (2007, background): “…logistic equation is a relatively simple example with easily studied dynamics that is useful in demonstrating the utility of the traditional sensitivity functions as well as the generalized sensitivity functions in inverse problems (see [9] for more discussions on TSF for this system).”