A Method for Evaluating the Informative Value of Arguments of a Nonparametric Stochastic Dependence Model with Their Specific Values

Abstract

A method for evaluating the informative value of arguments for unambiguous stochastic dependence at their specific values under conditions of a priori uncertainty is described. Taking into account the asymptotic properties of a nonparametric collective, a consistent procedure for forming its structure is proposed. The considered collective, by contrast with traditional nonparametric regression, takes into account not only the information contained in the observations of the variables of the reconstructed dependence but also the relationships between them. The peculiarity of the nonparametric collective of linear approximations of the desired dependence is the possibility of its representation in a form sufficient to assess the informative value of arguments according to their specific values. From these positions, a criterion for ranking the arguments of the function being restored according to their significance is defined.