Nonlinear predictive model selection and model averaging using information criteria Article Swipe

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Yuanlin Gu , Hua‐Liang Wei , M. Balikhin ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1080/21642583.2018.1496042

This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and determine which one best presents the data. Three commonly used criteria, namely, Akaike information criterion, Bayesian information criterion and an adjustable prediction error sum of squares (APRESS) are investigated and their performance in model selection and model averaging is evaluated via a number of case studies using both simulation and real data. The results show that APRESS produces better models in terms of generalization performance and model complexity.

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Akaike information criterion Bayesian information criterion Information Criteria Model selection Generalization Selection (genetic algorithm) Computer science Set (abstract data type) Nonlinear system Data set Data mining Bayesian probability Identification (biology) Series (stratigraphy) Machine learning Mathematics Artificial intelligence Botany Mathematical analysis Quantum mechanics Programming language Paleontology Biology Physics

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Type: article
Language: en
Landing Page: https://doi.org/10.1080/21642583.2018.1496042
PDF: https://www.tandfonline.com/doi/pdf/10.1080/21642583.2018.1496042?needAccess=true
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Cited By: 38
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DOI: https://doi.org/10.1080/21642583.2018.1496042

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Title: Nonlinear predictive model selection and model averaging using information criteria

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Language: en

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Publication year: 2018

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Publication date: 2018-01-01

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Authors: Yuanlin Gu, Hua‐Liang Wei, M. Balikhin

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Landing page: https://doi.org/10.1080/21642583.2018.1496042

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Concepts: Akaike information criterion, Bayesian information criterion, Information Criteria, Model selection, Generalization, Selection (genetic algorithm), Computer science, Set (abstract data type), Nonlinear system, Data set, Data mining, Bayesian probability, Identification (biology), Series (stratigraphy), Machine learning, Mathematics, Artificial intelligence, Botany, Mathematical analysis, Quantum mechanics, Programming language, Paleontology, Biology, Physics

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Cited by: 38

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