Robustness criteria

If robustness has to be build in, then the concept of robustness has to be formalized and optimized. This is contrary to the class of methods that check the robustness or ruggedness of existing methods then the influence 

Several ways to formalize the concept of robustness are presented in this book. Robustness can be formalized and expressed as a variance of the quality criterion which is done in Chapter 7. Another way to formalize robustness is the percentage change of the response, which is done in Chapter 8. It is also possible to express robustness in more complicated ways, examples of those are given in Chapters 2 and 4. In Chapter 6 a maxi-min formalization is chosen select the TLC-solvent composition in such a way that the minimum resolution between two pair of solutes is maximized. 

---

Finally, where a high risk has been assessed based on complexity, familiarity and robustness criteria a classical comparability assessment will be performed between both sites. This approach is summarised in Table 4 for a typical assay/impurity method (Raska et al., 2010). 

J.H. de Boer, Chemometrical Aspects of Quality in Pharmaceutical Technology. The application of robustness criteria and multi criteria decision making in... 

ROBUSTNESS CRITERIA INCORPORATING ROBUSTNESS EXPLICITLY IN OPTIMIZATION PROCEDURES UTILIZING MULTICRITERIA METHODS... 

The three robustness criteria that are explained here (Weighted-Jones (WJ), Projected-Variance (PV) and Robustness-Coefficient (RC)) describe all three the robustness of a certain mixture composition in direct relation to the response to be optimised. All three express the concept of robustness into a numerical value that can be calculated for each mixture setting (composition) of interest. So each of the criteria can be calculated as a function of the mixture composition and belongs directly to a certain response of interest. In this way a robustness criterion can be dealt with in a normal way in a mixture optimisation strategy. 

Validation of robustness criteria by means of a comparison with a simulation experiment... 

In order to assess the validity of the robustness criteria, which were derived in the previous section, an algorithm was developed for the validation of the criteria. This algorithm existed of two parts, the results of which were compared and consequently used to assess the robustness algorithm. 

Since the final form of a maximum likelihood estimator depends on the assumed error distribution, we partially answered the question why there are different criteria in use, but we have to go further. Maximum likelihood estimates are only guaranteed to have their expected properties if the error distribution behind the sample is the one assumed in the derivation of the method, but in many cases are relatively insensitive to deviations. Since the error distribution is known only in rare circumstances, this property of robustness is very desirable. The least squares method is relatively robust, and hence its use is not restricted to normally distributed errors. Thus, we can drop condition (vi) when talking about the least squares method, though then it is no more associated with the maximum likelihood principle. There exist, however, more robust criteria that are superior for errors with distributions significantly deviating from the normal one, as we will discuss... 

The constraints on the qualities De and Da are strongly dependent on the given data and the application in mind. However, it is reasonable to assume that the allowable Da is at least at the order of De, and in many cases even much larger. We use the ratio DA,min/De as a robustness criteria, with D.4,min being the minimal distortion for a successful attack. Chen and Womell [2] introduced the term distortion penalty for D A,min/D E. 

AU behaviours documented in requirements at these different levels of abstraction are verified, including with respect to robustness criteria. 

Completeness/Extensiveness Results of reviews and inspection of test descriptions and test cases, including the degree to which requirements coverage is achieved and satisfaction of normal and robustness test criteria have been met. Results of reviews and inspection of test results. Tool generation of test cases against nominated test case criteria, such as normal and robustness criteria. 

The determination of robustness criteria is facilitated by maps of critical resolution for two simultaneously varied parameters (Figs. 10 and 11). Such resolution maps can be derived from a systematic method development and can be represented without additional experiments using appropriate simulation software packages. 

---