Nonparametric analysis of stochastic systems with nonlinear functional heterogeneity

Abstract

The problems of the analysis of stochastic systems described by nonlinear statistical models with heterogeneous functional forms are considered. It is supposed that functional heterogeneity is conditioned by the existing of the different classes of system states. Moreover it being known that every state of a system characterized by un-known nonlinear models, that are different for different classes of states. The methods of estimation and forecasting of the systems states based on multivariate nonparametric density estimate with variable kernel are suggested and examined by means of asymptotic expansions of the conditional risk as well as by statistical modeling experiments.