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Model selection with data-oriented penalty. (English) Zbl 0926.62045
Summary: We consider the problem of model (or variable) selection in the classical regression model using the GIC (general information criterion). In this method the maximum likelihood is used with a penalty function denoted by $$C_n$$, depending on the sample size $$n$$ and chosen to ensure consistency in the selection of the true model. There are various choices of $$C_n$$ suggested in the literature on model selection. We show that a particular choice of $$C_n$$ based on observed data, which makes it random, preserves the consistency property and provides improved performance over a fixed choice of $$C_n$$.

MSC:
 62J05 Linear regression; mixed models 62F10 Point estimation
Keywords:
AIC; model selection; variables selection; GIC
Full Text:
References:
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