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Using a priori information in regression analysis. (English. Russian original) Zbl 1307.62189

Cybern. Syst. Anal. 49, No. 1, 41-54 (2013); translation from Kibern. Sist. Anal. 2013, No. 1, 49-64 (2013).
Summary: 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. Stochastic a priori information is systems of equations, which are linear in regression parameters and whose right-hand sides are random variables. 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 well-known methods. An evaluation method is developed, which combines the fuzzy and stochastic a priori information about regression parameters.

MSC:

62J86 Fuzziness, and linear inference and regression
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