About algorithm of robust nonparametric estimation of regression function on observations

The field of research presented in the paper is aimed at studying the methods of robust statistics for the modeling of multidimensional processes of discrete-continuous type. The model of the investigated process is constructed using identification methods. In one case, it is used parametric methods of identification where priori information about the object of research is sufficient to build the model accurate within vector of parameters. The second case is specific to lack of priori information. The researchers do not know the structure of the model and represent the object in the form of a "black box", therefore, nonparametric identification methods could be used. The accuracy of models is estimated using a relative error of approximation, which shows how much the model output value corresponds to the output value of the object. The paper proposes a new method of outliers' detection in the initial sample of observations, which is subsequently used for parametric and nonparametric identification of processes. The developed robust algorithm is applied to both types of models in order to determine in which case accuracy of outliers' detection is higher. In addition, the above algorithm is compared with an algorithm based on the interquartile range.