Model evaluation and selection in multiple nonlinear regression analysis

    Gints Jēkabsons Info
    Jurijs Lavendels Info
    Vjaceslavs Sitikovs Info

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

Keywords:

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

Share

Published in Issue
March 31, 2007
Abstract Views
567

View article in other formats

CrossMark check

CrossMark logo

Published

2007-03-31

Issue

Section

Articles

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

Share