Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 42, Issue 1-2, Pages 47-72Publisher
ELSEVIER
DOI: 10.1016/S0167-9473(02)00117-2
Keywords
fuzzy/crisp input data; fuzzy/crisp output data; linear regression analysis; unconstrained and constrained least-squares estimates
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In order to estimate fuzzy regression models, possibilistic and least-squares procedures can be considered. By taking into account a least-squares approach, regression models with crisp or fuzzy inputs and crisp or fuzzy output are suggested. In particular, for these fuzzy regression models, unconstrained and constrained (with inequality restrictions) least-squares estimation procedures are developed. Furthermore, for the various models presented, explanatory examples are shown and some concluding remarks are also included. (C) 2002 Elsevier Science B.V. All rights reserved.
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