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Publikācija: A Heuristic Approach of Model Selection in Multiple Nonlinear Regression Analysis

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Nosaukums oriģinālvalodā A Heuristic Approach of Model Selection in Multiple Nonlinear Regression Analysis
Pētniecības nozare 1. Dabaszinātnes
Pētniecības apakšnozare 1.2. Datorzinātne un informātika
Autori Gints Jēkabsons
Jurijs Lavendels
Atslēgas vārdi Regression, approximation, model selection, heuristic search methods
Anotācija 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.
Hipersaite: http://www.iadis.net/dl/final_uploads/200601C026.pdf 
Atsauce Jēkabsons, G., Lavendels, J. A Heuristic Approach of Model Selection in Multiple Nonlinear Regression Analysis. No: Proceedings of the IADIS International Conference "Applied Computing 2006", Spānija, San Sebastian, 4.-6. marts, 2006. San Sebastian: IADIS Press, 2006, 524.-527.lpp. ISBN 972-8924-09-7.
ID 3046