Fuzzy linear regression using rank transform method

被引:43
作者
Jung, Hye-Young [1 ]
Yoon, Jin Hee [2 ]
Choi, Seung Hoe [3 ]
机构
[1] Seoul Natl Univ, Dept Stat, Seoul 151742, South Korea
[2] Sejong Univ, Sch Math & Stat, Seoul 143747, South Korea
[3] Korea Aerosp Univ, Sch Liberal Arts & Sci, Koyang 411, South Korea
基金
新加坡国家研究基金会;
关键词
PROGRAMMING APPROACH; OUTLIERS DETECTION; MODELS; INPUT;
D O I
10.1016/j.fss.2014.11.004
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In regression analysis, the rank transform (RT) method is known to be neither dependent on the shape of the error distribution nor sensitive to outliers. In this paper, we construct a so-called a-level fuzzy regression model based on the resolution identity theorem and apply RT method to this model. Fuzzy regression models with crisp input/fuzzy output and fuzzy input/fuzzy output are investigated to show the effectiveness of the proposed method. To compare its effectiveness with existing methods, we introduce a new performance measure. In addition, we propose a method to obtain a predicted output with respect to a specific target value and show that our model is more robust compared with other methods when the data contain some outliers. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:97 / 108
页数:12
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