Information criterion for Gaussian change-point model

Yoshiyuki Ninomiya

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)237-247
Number of pages11
JournalStatistics and Probability Letters
Issue number3
Publication statusPublished - May 1 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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