Copyright © 2006 Elsevier B.V. All rights reserved.
Regression with fuzzy random data
Available online 20 March 2006.
References and further reading may be available for this article. To view references and further reading you must purchase this article.
Abstract
Different approaches to deal with regression analysis when the data are fuzzy are presented. It summarizes recent results and considers them in a more general context which allows to evaluate the different methods. Starting with necessary notions on regression and on fuzzy sets, three approaches are presented: at first a pure descriptive statistical approach, secondly statistical regression when the output is modeled by a fuzzy random variable (FRV) and finally regression between two FRVs.
Keywords: Regression; Fuzzy random variable; Best linear unbiased estimation; Least squares
Article Outline
- 1. Introduction
- 2. Classical regression
- 2.1. Regression as fitting or approximation problem (descriptive statistical regression)
- 2.2. Statistical regression using a stochastic model for approximate functional relationships
- 2.3. Regression between two random variables (nonstatistical (theoretical) regression)
- 3. Some notions from fuzzy set theory
- 3.1. Fuzzy sets
- 3.2. Distances between fuzzy sets
- 4. Descriptive regression with fuzzy data
- 4.1. Transfer principles
- 4.2. Fuzzy least squares
- 4.3. Application of the extension principle
- 4.4. Best covering fuzzy function
- 5. Statistical regression with fuzzy data
- 5.1. Fuzzy random variables of second order
- 5.2. BLUE
- 5.3. Weak BLUE
- 5.4. Componentwise BLUE
- 5.5. Extended BLUE
- 6. Regression between two FRVs
- References






E-mail Article
Add to my Quick Links

Cited By in Scopus (2)

max(




