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- W1760378052 abstract "The aim of this paper is to study the penalty functions of the well-known model selection criteria, AIC , BIC , and KIC , which can unify their formulas as APIC = log( 2)+ (p+1)/n, called Adjusted Penalty Information Criterion. The appropriate value of for APIC has been found to reduce the probabilities of over- and underfitting and also to overcome the weak signal-to-noise ratio. The value of is selected based on four measurements: the probability of over- and underfitting, the signal-to-noise ratio, the probability of order selected, and the observed L 2 efficiency. Performance of APIC is examined by theoretical and extensive simulation study. The theoretical results show that, the probability of overfitting tends to zero and the signal-to-noise ratio tends to strong if the value of tends to infinity. However, the simulation results show that, when the true model is weakly identifiable, the small value of gives a high probability of correct order being selected. But, if the true model is very difficult to detect, the observed L 2 efficiency is a meaningful measurement than the probability of order selected. The observed L 2 efficiency suggests the large value of causes the high efficiency of APIC which indicates that the correct model is also the closet model, except when the true model can be specified more easily and sample sizes are moderate to large, then the small value of is preferable. For the strongly identifiable true model, the large value of performs well, whereas if the regression coefficients are not large enough and the sample sizes are small to moderate, the value of should be moderate." @default.
- W1760378052 created "2016-06-24" @default.
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- W1760378052 date "2014-01-01" @default.
- W1760378052 modified "2023-09-23" @default.
- W1760378052 title "Study on the penalty functions of model selection criteria" @default.
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