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Selection of simplified models: II. Development of a model selection criterion based on mean squared error
Author(s) -
Wu Shaohua,
McAuley K. B.,
Harris T. J.
Publication year - 2011
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20479
Subject(s) - bayesian information criterion , univariate , model selection , mean squared error , selection (genetic algorithm) , monte carlo method , information criteria , multivariate statistics , set (abstract data type) , mathematics , statistics , computer science , nonlinear system , deviance information criterion , estimation theory , bayesian probability , algorithm , markov chain monte carlo , machine learning , physics , quantum mechanics , programming language
Simplified models (SMs) with a reduced set of parameters are used in many practical situations, especially when the available data for parameter estimation are limited. A variety of candidate models are often considered during the model formulation, simplification, and parameter estimation processes. We propose a new criterion to help modellers select the best SM, so that predictions with lowest expected mean squared error can be obtained. The effectiveness of the proposed criterion for selecting simplified nonlinear univariate and multivariate models is demonstrated using Monte‐Carlo simulations and is compared with the effectiveness of the Bayesian Information Criterion (BIC).