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Linear regression analysis for fuzzy input and output data using the extension principle

descriptionPublicationkeyboard_double_arrow_right Article 01 Jun 2003 English Publisher:Elsevier BVJournal:Computers & Mathematics with Applications, volume 45, pages 1,849-1,859 (issn: 0898-1221, Copyright policy )
Authors: Hsien-Chung Wu;

doi: 10.1016/s0898-1221(03)90006-x

Linear regression analysis for fuzzy input and output data using the extension principle

Abstract

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Keywords

Optimization, Linear regression; mixed models, Computational problems in statistics, fuzzy numbers, Fuzzy numbers, Computational Mathematics, Linear inference, regression, Computational Theory and Mathematics, fuzzy least squares estimator, Modelling and Simulation, Fuzzy least squares estimator, optimization

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    popularity
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    influence
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
52
Top 10%
Top 10%
Average
hybrid