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A Regression Model Based on Uncertain Set

Asian Business Research

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Title A Regression Model Based on Uncertain Set
 
Creator Li, Xiaona
Wang, Xiaosheng
Sampath, Sundaram
Li, Mingchao
Wang, Jiawei
 
Description Traditional regression analysis is a method of statistical data analysis based on probability theory. Regression models play crucial roles in various branches of statistics including design of experiments, econometrics etc. In regression models, the dependent variable is assumed to be of stochastic nature where randomness enters via errors. Further, the independent variables are assumed to be of deterministic nature. The regression coefficients which explain the interdependency between the variables are assumed to be crisp quantities. Whenever, difficulty arises in expressing the values taken by the dependent variable in terms of crisp quantities, traditional regression models become irrelevant. This paper provides a framework for dealing with such situations on using the notion of uncertain sets of various forms. In this paper, a solution for this problem obtained via linear programming technique is introduced along with an illustrative example.
 
Publisher July Press Pte. Ltd.
 
Contributor —
 
Date 2017-11-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://journal.julypress.com/index.php/abr/article/view/215
10.20849/abr.v2i3.215
 
Source Asian Business Research; Vol 2, No 3 (2017); p33
2424-8983
2424-8479
 
Language eng
 
Relation http://journal.julypress.com/index.php/abr/article/view/215/182
 
Rights Copyright (c) 2017 Xiaona Li, Xiaosheng Wang, Sundaram Sampath, Mingchao Li, Jiawei Wang
http://creativecommons.org/licenses/by/4.0