The paper presents a new approach to defining the relationships between small amounts of input and output data. This approach proceeds from involving randomized (static and dynamic) models and estimating the probabilistic characteristics of their random parameters. We consider static and dynamic models described by Volterra polynomials. The procedures of robust parametric and non-parametric estimation are constructed by exploiting the entropy concept based on the generalized informational Fermi–Dirac entropy and the generalized informational Boltzmann entropy.