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A New Criterion for Model Selection

Author

Listed:
  • Hoang Pham

    (Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA)

Abstract

Selecting the best model from a set of candidates for a given set of data is obviously not an easy task. In this paper, we propose a new criterion that takes into account a larger penalty when adding too many coefficients (or estimated parameters) in the model from too small a sample in the presence of too much noise, in addition to minimizing the sum of squares error. We discuss several real applications that illustrate the proposed criterion and compare its results to some existing criteria based on a simulated data set and some real datasets including advertising budget data, newly collected heart blood pressure health data sets and software failure data.

Suggested Citation

  • Hoang Pham, 2019. "A New Criterion for Model Selection," Mathematics, MDPI, vol. 7(12), pages 1-12, December.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1215-:d:296053
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    References listed on IDEAS

    as
    1. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, December.
    2. Qiuying Li & Hoang Pham, 2017. "A testing-coverage software reliability model considering fault removal efficiency and error generation," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-25, July.
    3. Kwang Yoon Song & In Hong Chang & Hoang Pham, 2019. "A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis," Mathematics, MDPI, vol. 7(5), pages 1-21, May.
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    Cited by:

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