THE SOFT REGRESSION METHOD- SUGGESTED IMPROVEMENTS

Eli Shnaider, Nava Haruvy and Arthur Yosef
Additional contact information
Eli Shnaider: Netanya Academic College 1 University St., Netanya, Israel
Nava Haruvy: Netanya Academic College 1 University St., Netanya, Israel
Arthur Yosef: Tel Aviv-Yaffo Academic College, 2 Rabenu Yeruham st., Tel Aviv-Yaffo, Israel

Fuzzy Economic Review, 2014, vol. XIX, issue 2, 21-33

Abstract: Soft regression is a regression technique based on fuzzy information processing and heuristic information processing methods. However, following the practical use of the method, it was found that two aspects of the calculations pertaining to the relative weights of explanatory variables could be improved to make the method more logical, and therefore more in line with heuristic basis of soft regression. In the following paper, we will present the soft regression method, the suggested improvements, and finally an example to illustrate the suggested improvements regarding factors affecting international growth and development.

Keywords: soft regression; similarity of numerical vectors; relative weight of explanatory variables; estimation of growth and development (search for similar items in EconPapers)
JEL-codes: C13 C20 (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fzy:fuzeco:v:xix:y:2014:i:2:p:21-33

Access Statistics for this article

More articles in Fuzzy Economic Review from International Association for Fuzzy-set Management and Economy (SIGEF) Contact information at EDIRC.
Bibliographic data for series maintained by Aurelio Fernandez ( this e-mail address is bad, please contact ).