0

Full Content is available to subscribers

Subscribe/Learn More  >

A Comparative Analysis and Evaluation of Model Selection Criteria

[+] Author Affiliations
Ahmed H. Kamel, Arslan Rozyyev

University of Texas of the Permian Basin, Odessa, TX

Ali S. Shaqlaih

University of North Texas at Dallas, Dallas, TX

Paper No. IMECE2017-70152, pp. V005T06A015; 10 pages
doi:10.1115/IMECE2017-70152
From:
  • ASME 2017 International Mechanical Engineering Congress and Exposition
  • Volume 5: Education and Globalization
  • Tampa, Florida, USA, November 3–9, 2017
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5840-0
  • Copyright © 2017 by ASME

abstract

The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper way to proceed to select the appropriate model for prediction.

The authors present an evaluation of various model selection criteria from decision-theoretic perspective using experimental data to define and recommend a criterion to select the best model. In this analysis, six of the most common selection criteria, nineteen friction factor correlations, and eight sets of experimental data are employed. The results show that while the use of the traditional correlation coefficient, R2 is inappropriate, root mean square error, RMSE can be used to rank models, but does not give much insight on their accuracy. Other criteria such as correlation ratio, mean absolute error, and standard deviation are also evaluated. The Akaike information criterion, AIC has shown its superiority to other selection criteria. The authors propose AIC as an alternative to use when fitting experimental data or evaluating existing correlations. Indeed, the AIC method is an information theory based, theoretically sound and stable. The paper presents a detailed discussion of the model selection criteria, their pros and cons, and how they can be utilized to allow proper comparison of different models for the best model to be inferred based on sound mathematical theory. In conclusion, model selection is an interesting problem and an innovative strategy to help alleviate similar challenges faced by the professionals in the oil and gas industry is introduced.

Copyright © 2017 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In