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A Heuristic Approach of Model Selection in Multiple Nonlinear Regression Analysis

By Gints Jēkabsons and Jurijs Lavendels

Abstract

This paper reflects a research goal of which is to develop heuristic approach for multiple nonlinear regression analysis model selection. From sixteen heuristic search algorithms suitable for multiple nonlinear regression analysis eight most popular algorithms were considered. All of the algorithms were classified and empirically evaluated from the aspect of both necessary computing resources and optimality of the results. The theoretical results of the research are implemented in software, which was used for approbation of the described approach in construction behavior modeling applications at Institute of Materials and Structures, Riga Technical University. Models obtained were more effective than previously used. Developed software is effective and competitive tool for solving practical regression problems

Topics: Regression, approximation, model selection, heuristic search methods
Publisher: IADIS Press
OAI identifier: oai:ortus.rtu.lv:3046
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