A Regression Model Based on Uncertain Set

Xiaona Li, Xiaosheng Wang, Sundaram Sampath, Mingchao Li, Jiawei Wang


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.

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DOI: https://doi.org/10.20849/abr.v2i3.215


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