Higher level fuzzy numbers arising from fuzzy regression models

Diamond P. (1990) Higher level fuzzy numbers arising from fuzzy regression models. Fuzzy Sets and Systems, 36 2: 265-275. doi:10.1016/0165-0114(90)90184-8

Author Diamond P.
Title Higher level fuzzy numbers arising from fuzzy regression models
Journal name Fuzzy Sets and Systems   Check publisher's open access policy
ISSN 0165-0114
Publication date 1990-06-25
Sub-type Article (original research)
DOI 10.1016/0165-0114(90)90184-8
Volume 36
Issue 2
Start page 265
End page 275
Total pages 11
Subject 1702 Cognitive Sciences
1706 Computer Science Applications
1707 Computer Vision and Pattern Recognition
1802 Maori Law
1804 Statistics, Probability and Uncertainty
2208 Electrical and Electronic Engineering
2613 Statistics and Probability
Abstract Consider a fuzzy random variable Y, with expectation θ{symbol} = B + βX, where B is an unknown fuzzy number and β an unknown real number. For N observations Yi, Xi there is a model Yi = B + βXi + Ei, i = 1, 2, ..., N, where Ei are fuzzy valued errors, independently and identically distributed in some sense. The aim is to obtain estimates of β, B. When all fuzzy numbers are triangular and the Ei are uniformly distributed, maximum likelihood estimators emerge naturally as a special form of higher level fuzzy number. Thus MLE can be interpreted as an estimation procedure which transforms randomness into higher levels of fuzziness.
Keyword estimation
Higher level fuzziness
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: Scopus Import
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Citation counts: Scopus Citation Count Cited 9 times in Scopus Article | Citations
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Created: Tue, 12 Jul 2016, 02:00:42 EST by System User