Thus, an efficacious data converter should be capable of protecting the harvested information by confronting extremities with robust filters. Possessing a high breakdown point is imperative for a profiler of a small and dense dataset such that to recognize irregularities of unknown origin and then suppress them. The selection of the sample median features a robust location estimator with a maximum achievable breakdown point of 50%. It is an efficient and economical estimator because it requires merely ordering a group of observations. The fact that a method utilizes known reference distributions that possess superior power properties while resisting to surrender accuracy to adverse situations are aspects highly appreciated in profiling. Finally, the method should be flexible and liberal enough to avert the entrapment that may be elicited by the sparsity assumption, i.e. the a priori restriction that not all of the examined effects are permitted to be either all weak or all strong. A superb nonlinear profiler should fend off variation leakage from the uncertainty term when gauging the CYT387 strength for each particular effect. For the non-linear unreplicated-saturated OAs, this becomes of paramount importance because the variation due to the uncertainty retains a cryptic character in absence of any degrees of freedom. The method we develop in this article is suitable for explaining stochastically non-linear saturated-unreplicated OA-datasets to be used for profiling concurrent tasks in a high-demand process, such as featured in the improvement of the AP-PCR performance. The technique promotes: 1) the decomposition of multi-factorials to corresponding single-effect surrogates, 2) the subsequent one-way contrasting for sizing the strength for each individual surrogate effect, and 3) a built-in detector for performing an internal-error consistency check. Using the novel surrogate response concept, the proposed method demonstrates that does not require the creation of new reference distributions. For the developments of the new ideas that will be stipulated in this article, we define as profiler, the screening device that allows the three-point tracing of an examined effect. Similarly, the meaning of extraction is congruent to the process of information harvesting. Finally, in accord with the previous two conventions, the term “quantification” assumes the stochastic interpretation of determining uncertainty. We presented a novel assumption-free technique for dealing with dense datasets suitable for profiling effects with potentially curvature tendencies such as in an AP-PCR procedure. To avoid confronting directly the pooled-error determination, we proposed an additive non-linear model for screening saturatedunreplicated OA-data. We built our model around a pivotal baseline where the partial effects may be stacked atop each other while granting an uncertainty term. Such a model facilitates the decomposition of a densely-compacted dataset during the information extraction phase. We defined the partial effect at a given setting to be the disparity of each effect’s median estimation from the baseline value.