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Model evaluation and selection in multiple nonlinear regression analysis

    Gints Jēkabsons Affiliation
    ; Jurijs Lavendels Affiliation
    ; Vjaceslavs Sitikovs Affiliation

Abstract

The main problem in regression model selection is finding the best model that best fits the data, i.e. it does not neither overfit nor underfit. The aim of this work is to show one of possible ways to find adequate nonlinear regression models (parametric) of technical systems based on an heuristic search and analytical optimality evaluation approach by taking into consideration the computational power of modern computers.


First Published Online: 14 Oct 2010

Keyword : Regression, approximation, model selection, heuristic search, model evaluation

How to Cite
Jēkabsons, G., Lavendels, J., & Sitikovs, V. (2007). Model evaluation and selection in multiple nonlinear regression analysis. Mathematical Modelling and Analysis, 12(1), 81-90. https://doi.org/10.3846/1392-6292.2007.12.81-90
Published in Issue
Mar 31, 2007
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