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Fault injection is a testing method that can be used for checking the robustness of systems. During the process, testing engineers inject faults into systems and observe the system's resiliency. [4] Test engineers can develop efficient methods which aid fault injection to find critical faults in the system. [5] [6]
Robust statistical methods, of which the trimmed mean is a simple example, seek to outperform classical statistical methods in the presence of outliers, or, more generally, when underlying parametric assumptions are not quite correct. Whilst the trimmed mean performs well relative to the mean in this example, better robust estimates are available.
The method proceeds by finding a highly robust and resistant S-estimate that minimizes an M-estimate of the scale of the residuals (the first M in the method's name). The estimated scale is then held constant whilst a close by M-estimate of the parameters is located (the second M).
Therefore, the choice of method of sensitivity analysis is typically dictated by a number of problem constraints, settings or challenges. Some of the most common are: Computational expense: Sensitivity analysis is almost always performed by running the model a (possibly large) number of times, i.e. a sampling -based approach. [ 8 ]
Robustification as it is defined here is sometimes referred to as parameter design or robust parameter design (RPD) and is often associated with Taguchi methods.Within that context, robustification can include the process of finding the inputs that contribute most to the random variability in the output and controlling them, or tolerance design.
Robust measures of scale can be used as estimators of properties of the population, either for parameter estimation or as estimators of their own expected value.. For example, robust estimators of scale are used to estimate the population standard deviation, generally by multiplying by a scale factor to make it an unbiased consistent estimator; see scale parameter: estimation.
Taguchi methods (Japanese: タグチメソッド) are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to engineering, [1] biotechnology, [2] [3] marketing and advertising. [4]
Robustness is the property of being strong and healthy in constitution. When it is transposed into a system, it refers to the ability of tolerating perturbations that might affect the system's functional body. In the same line robustness can be defined as "the ability of a system to resist change without adapting its initial stable ...