Title : ( A least-absolutes approach to multiple fuzzy regression )
Authors: J. Chachi , Seed Mahmood Taheri ,
Full TextAbstract
A least-absolutes approach to multiple fuzzy regression modeling is introduced and investigated for the case of crisp input - fuzzy output data, by using the generalized Hausdorff-metric. A comparative study, based on three data set, including a real agricultural data set, and three well-known goodness of fit indices indicate that the proposed approach has certain advantages to some common methods in fuzzy regression modeling.
Keywords
, Fuzzy regression; Hausdorff, metric; Imprecise data; Least, absolutes method
@inproceedings{paperid:1027850,
author = {J. Chachi and Taheri, Seed Mahmood},
title = {A least-absolutes approach to multiple fuzzy regression},
booktitle = {58th World Statistics Congress of the ISI (ISI2011)d},
year = {2011},
location = {Dublin},
keywords = {Fuzzy regression; Hausdorff-metric; Imprecise data; Least-absolutes method},
}
%0 Conference Proceedings
%T A least-absolutes approach to multiple fuzzy regression
%A J. Chachi
%A Taheri, Seed Mahmood
%J 58th World Statistics Congress of the ISI (ISI2011)d
%D 2011
