Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10525961 | Statistics & Probability Letters | 2005 | 11 Pages |
Abstract
AIC-type information criterion is generally estimated by the bias-corrected maximum log-likelihood. In regular models, the bias can be estimated by p, where p is the number of parameters. The present paper considers the AIC-type information criterion for change-point models which are not regular, the bias of which will not be the same as for regular models. The bias is shown to depend on the expected maximum of a random walk with negative drift. Furthermore, it is shown that by using an approximation to a Brownian motion, the evaluated bias is given by 3m+pm (not m+pm), where m is the number of change-points and pm is the number of regular parameters, which differs from regular models.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Yoshiyuki Ninomiya,