Using a priori information in regression analysis

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 equati...

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Bibliographic Details
Published in:Cybernetics and systems analysis Vol. 49; no. 1; pp. 41 - 54
Main Author: Korkhin, A. S.
Format: Journal Article
Language:English
Published: Boston Springer US 01.01.2013
Springer
Springer Nature B.V
Subjects:
Artificial Intelligence
Classification
Constants
Control
Estimating techniques
Fuzzy
Fuzzy logic
Inequality
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Parameters
Processor Architectures
Random variables
Regression
Regression analysis
Software Engineering/Programming and Operating Systems
Stochasticity
Studies
Systems Theory
two-criteria estimation
combined methods of estimation
fuzzy constraints
mixed regression
a priori information
stationary and nonstationary regressions
ISSN:1060-0396, 1573-8337
Online Access:Get full text
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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.
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ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-013-9483-6