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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Veröffentlicht in:Cybernetics and systems analysis Jg. 49; H. 1; S. 41 - 54
1. Verfasser: Korkhin, A. S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.01.2013
Springer
Springer Nature B.V
Schlagworte:
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
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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. 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.
AbstractList 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.
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. Keywords: stationary and nonstationary regressions, a priori information, fuzzy constraints, two-criteria estimation, mixed regression, combined methods of estimation.
Audience Academic
Author Korkhin, A. S.
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CitedBy_id crossref_primary_10_1108_JERER_11_2020_0057
crossref_primary_10_1016_j_compchemeng_2014_11_010
Cites_doi 10.1007/BF01068320
10.1080/01621459.1966.10502016
10.1016/0304-4076(82)90003-3
10.2307/2525589
10.1080/01621459.1976.10481560
10.1007/BF01082610
10.1080/01621459.1963.10500854
10.1007/978-3-7908-2064-5_22
10.1002/9780471722199
10.1080/00224065.1999.11979917
ContentType Journal Article
Copyright Springer Science+Business Media New York 2013
COPYRIGHT 2013 Springer
Cybernetics and Systems Analysis is a copyright of Springer, 2013.
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Keywords two-criteria estimation
combined methods of estimation
fuzzy constraints
mixed regression
a priori information
stationary and nonstationary regressions
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Snippet The paper considers the methods to evaluate regression parameters under indefinite a priori information of two types: fuzzy and stochastic. Fuzzy a priori...
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StartPage 41
SubjectTerms 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
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Title Using a priori information in regression analysis
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Volume 49
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