Information criterion for Gaussian change-point model

Yoshiyuki Ninomiya

Statistics & Probability Letters, 2005, vol. 72, issue 3, 237-247

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.

Keywords: Akaike's; information; criterion; Brownian; motion; with; drift; Maximum; of; random; walk; Number; of; change-points (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (11)

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