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Model selection in applied science and engineering / R. V. Field in Journal of engineering mechanics, Vol. 133 N°7 (Juillet 2007) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 133 N°7 (Juillet 2007) . - pp.780–791. Titre : Model selection in applied science and engineering : a decision-theoretic approach Type de document : texte imprimé Auteurs : R. V. Field, Auteur ; Mircea Grigoriu, Auteur Année de publication : 2007 Article en page(s) : pp.780–791. Note générale : Applied mechanics Langues : Anglais (eng) Mots-clés : Bayesian  analysis  Probability  Mathematical  models  System  analysis Résumé : Mathematical models are developed and used to study the properties of complex systems in just about every area of applied science and engineering. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. A decision-theoretic method is developed for selecting the optimal member from the collection. The optimal model depends on the available information, the class of candidate models, and the model use. The candidate models may be deterministic or random. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, are briefly reviewed. These methods ignore model use and require data to be available. In addition, examples are used to show that classical methods for model selection can be unreliable in the sense that they can deliver unsatisfactory models when data is limited. The proposed decision-theoretic method for model selection does not have these limitations. The method accounts for model use via a utility function. This feature is especially important when modeling high-risk systems where the consequences of using an inappropriate model for the system can be disastrous. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282007%29133%3A7%2878 [...] [article] Model selection in applied science and engineering : a decision-theoretic approach [texte imprimé] / R. V. Field, Auteur ; Mircea Grigoriu, Auteur . - 2007 . - pp.780–791. Applied mechanics Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 133 N°7 (Juillet 2007) . - pp.780–791. Mots-clés : Bayesian  analysis  Probability  Mathematical  models  System  analysis Résumé : Mathematical models are developed and used to study the properties of complex systems in just about every area of applied science and engineering. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. A decision-theoretic method is developed for selecting the optimal member from the collection. The optimal model depends on the available information, the class of candidate models, and the model use. The candidate models may be deterministic or random. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, are briefly reviewed. These methods ignore model use and require data to be available. In addition, examples are used to show that classical methods for model selection can be unreliable in the sense that they can deliver unsatisfactory models when data is limited. The proposed decision-theoretic method for model selection does not have these limitations. The method accounts for model use via a utility function. This feature is especially important when modeling high-risk systems where the consequences of using an inappropriate model for the system can be disastrous. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282007%29133%3A7%2878 [...]