Comparison and Assessment of Commonly Used Model Selection Criteria in Modeling of Experimental Data
DOI:
https://doi.org/10.11145/cb.v3i1.647Abstract
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 [1]. Different criteria for evaluation of competitive mathematical models for data fitting have become available [2], [3].
This research has several specific objectives: (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. ...Downloads
Published
Issue
Section
License
The journal Biomath Communications is an open access journal. All published articles are immeditely available online and the respective DOI link activated. All articles can be access for free and no reader registration of any sort is required. No fees are charged to authors for article submission or processing. Online publications are funded through volunteer work, donations and grants.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
