Title : ( Fuzzy linear regression based on least absolute deviations )
Authors: Seed Mahmood Taheri , M. Kelkinnama ,
Full TextAbstract
This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by three goodness of t criteria. Three well-known data sets including two small data sets as well as a large data set are employed for such comparisons.
Keywords
, Fuzzy regression, Least absolutes deviations, Metric on fuzzy numbers, Similarity measure, Goodness of fit.
@article{paperid:1027617,
author = {Taheri, Seed Mahmood and M. Kelkinnama},
title = {Fuzzy linear regression based on least absolute deviations},
journal = {Iranian Journal of Fuzzy Systems},
year = {2012},
volume = {9},
number = {1},
month = {January},
issn = {1735-0654},
pages = {121--140},
numpages = {19},
keywords = {Fuzzy regression; Least absolutes deviations; Metric on fuzzy numbers;
Similarity measure; Goodness of fit.},
}
%0 Journal Article
%T Fuzzy linear regression based on least absolute deviations
%A Taheri, Seed Mahmood
%A M. Kelkinnama
%J Iranian Journal of Fuzzy Systems
%@ 1735-0654
%D 2012
