In the previous paper by the authors Linke et al. Mathematics. — V.12(12). 1890 (2024), for a wide class of nonparametric regression models, new kernel-type estimators belonging to the class of local linear ones were proposed. The kernel estimators from Linke et al. Mathematics. — V.12(12). 1890 (2024) are universal with respect to design constraints that ensure uniform consistency of this new estimators. In Linke et al. Mathematics. — V.12(12). 1890 (2024), all that is required is an asymptotically dense filling of the domain of the regression function with design elements when the sample size tends to infinity. This condition is essentially necessary to restore the regression function and includes the cases of both fixed design, at the same time, without the requirement of its regularity, and random one, and not necessarily consisting of independent or weakly dependent random variables. In the present paper, the results from Linke et al. Mathematics. — V.12(12). 1890 (2024) are illustrated by computer modeling and examples of processing real numerical data from the field of medicine and are compared with several other estimation approaches. In all the numerical examples, the new universal local linear estimators exhibited robust performance, in contrast to certain common models which occasionally showed inflated errors.
