Uncertain regression analysis: an approach for imprecise observations

Regression analysis is a method to estimate the relationships among the response variable and the explanatory variables. Assuming the observations of the response variable are imprecise and modeling the observed data via uncertain variables, this paper explores an approach of uncertain regression an...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2018-09, Vol.22 (17), p.5579-5582
Main Authors: Yao, Kai, Liu, Baoding
Format: Article
Language:English
Subjects:
Algebra
Artificial Intelligence
Computational Intelligence
Control
Engineering
Expected values
Focus
Knowledge management
Mathematical Logic and Foundations
Mechatronics
Parameters
Probability distribution
Questionnaires
Regression analysis
Regression models
Robotics
Uncertainty analysis
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Regression analysis is a method to estimate the relationships among the response variable and the explanatory variables. Assuming the observations of the response variable are imprecise and modeling the observed data via uncertain variables, this paper explores an approach of uncertain regression analysis to estimating the relationships among the variables with imprecisely observed samples. On the principle of least squares, an optimization problem is derived to calculate the unknown parameters in the regression model. In particular, this paper investigates uncertain linear regression model and gives an analytic representation of the unknown parameters.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-017-2521-y