In this paper we develop an approach to this issue using a method based on the Girsanov measure transformation for jump processes to smooth the estimate of the sensitivity coefficients and make this estimation more accurate.
To make this estimation one gets where represents the marginal variance of noisy observations and.
Multicollinearity issues and a high number of regressors might make this estimation procedure unstable.
This makes this estimation robust against illumination artifacts (such as shadows, which increase or decrease local illumination but do not affect local ratios so dramatically).
This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for purification process.
This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for fermentation monitoring and control.
To make this estimation more robust, cumulants of different order (mainly the second and the fourth order) can be used to identify such parts.
Agreement should be reached at a local level on how to make this estimation available.
Further accumulation of receptor imaging data would make this estimation more precise.
To make this estimation the authors consider the correlation between the rates in single domains and the corresponding rate in multi-domain proteins.
