Abstract. The paper considers the methods to evaluate regression parameters under indefinite a priori information of two types: fuzzy and stochastic. Fuzzy a priori information is assumed to be formulated on the basis of fuzzy notions of the model designer. The stochastic a priori information is systems of equations, which are linear in regression parameters and whose right-hand sides are random variables. The regression parameters may both be constant and vary in time. A classification of the evaluation methods using indefinite a priori information is proposed and used to generalize the well-known methods. An evaluation method is developed, which combines the fuzzy and stochastic a priori information about regression parameters.

Keywords: stationary and nonstationary regressions, a priori information, fuzzy constraints, two-criteria estimation, mixed regression, combined methods of estimation.