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Open AccessArticle

A New Criterion for Model Selection

Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA
Mathematics 2019, 7(12), 1215; https://doi.org/10.3390/math7121215
Received: 5 November 2019 / Revised: 2 December 2019 / Accepted: 5 December 2019 / Published: 10 December 2019
(This article belongs to the Special Issue Statistics and Modeling in Reliability Engineering)
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. View Full-Text
Keywords: model selection; criterion; statistical criteria model selection; criterion; statistical criteria
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Pham, H. A New Criterion for Model Selection. Mathematics 2019, 7, 1215.

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