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
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (109864 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 2754 times since July 24, 2006.
Return to the Home Page.