Selecting Selection Methods
by Erhard Reschenhofer.
This paper proposes a new approach for model selection and
applies it to a classical time series modeling problem. In contrast to
conventional model selection methods like AIC and BIC, whose penalty terms
typically depend only on the number of model parameters, the proposed
model selection method also takes the values of the model parameters and
the sets of candidate models into account. A brief sketch of a Bayesian
further development of this method is given within the framework of the
linear regression model.
AIC, BIC, Model Selection Criteria
Erhard Reschenhofer, firstname.lastname@example.org
Bradley T. Ewing, email@example.com
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