A strong consistent least-squares estimator in a linear fuzzy regression model with fuzzy parameters and fuzzy dependent variables

被引：9
|
作者：
Stahl, Christoph [1 ]
机构：
[1] Univ Saarland, Fac Law & Econ, D-66041 Saarbrucken, Germany
来源：
FUZZY SETS AND SYSTEMS | 2006年 / 157卷 / 19期
关键词：
least-squares estimation; identification of parameters; strong consistency; L-2-metric; T-dimensional fuzzy random variables; Aumann expected value; Frechet variance;
D O I：
10.1016/j.fss.2003.04.001
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper a linear fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. This is an extension of the ordinary linear regressions models by integrating physical and/or epistemical vaqueness to the dependent variables and as a consequence to the parameters. Within this paper the least-squares method is used to obtain an estimate for the fuzzy parameters in a statistical sense. Furthermore, we give a statistical justification of the proposed method by proving that the extended least-squares estimator is a strong consistent estimator. (c) 2006 Elsevier B.V. All rights reserved.
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页码：2593 / 2607
页数：15
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