Modeling of socially significant respondents' behavior: analytical and numerical rate estimates based on the episodes near interview in case of information deficiency

The paper offers a method for respondents' behavior modeling based on data about last episodes adjacent to interview and proposes improved techniques of modeling and processing of initial data uncertainty based on hybrid probabilistic and fuzzy approaches. This paper provides analytical (including asymptotic analysis of these estimates) and numerical behavior rate estimates according to the model. The developed software calculates estimates according to the considered models and supports making numerical experiment.