AUTHORS: Radoslav Mavrevski, Peter Milanov, Metodi Traykov, Nevena Pencheva
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ABSTRACT: Model selection is a process of choosing a model from a set of candidate models which will provide the best balance between goodness of fit of the data and complexity of the model. Different criteria for evaluation of competitive mathematical models for data fitting have become available. The main objectives of this study are: (1) to generate artificial experimental data by known models; (2) to fit data with various models with increasing complexity; (3) to verify if the model used to generate the data could be correctly identified through the two commonly used criteria Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) and to assess and compare empirically their performance. The artificial experimental data generating and the curve fitting is performed through using the GraphPad Prism software
KEYWORDS: model selection criteria, fitting, experimental data
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