Abstract
A method for hybrid least-squares regression, based on the weighted fuzzy arithmetic and the least-squares fitting criterion, is developed in this chapter. Both bivariate regression model and multiple regression model are derived and developed. Two numerical examples are used to demonstrate the proposed method. In each example, hybrid regression equations and their reliability measures are calculated. Furthermore, hybrid least-squares regression is extended to nonlinear models. At the end, conclusions are drawn based on the reliability evaluations.
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References
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Chang, YH.O., Ayyub, B.M. (1998). Hybrid Least-Squares Regression Analysis. In: Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach . International Series in Intelligent Technologies, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5473-8_12
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DOI: https://doi.org/10.1007/978-1-4615-5473-8_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7500-5
Online ISBN: 978-1-4615-5473-8
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