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Information criterion for Gaussian change-point model

Listed author(s):
  • Ninomiya, Yoshiyuki
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    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.

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    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 72 (2005)
    Issue (Month): 3 (May)
    Pages: 237-247

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    Handle: RePEc:eee:stapro:v:72:y:2005:i:3:p:237-247
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    1. Yao, Yi-Ching, 1988. "Estimating the number of change-points via Schwarz' criterion," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 181-189, February.
    2. Stryhn, Henrik, 1996. "The location of the maximum of asymmetric two-sided Brownian motion with triangular drift," Statistics & Probability Letters, Elsevier, vol. 29(3), pages 279-284, September.
    3. Lee, Chung-Bow, 1995. "Estimating the number of change points in a sequence of independent normal random variables," Statistics & Probability Letters, Elsevier, vol. 25(3), pages 241-248, November.
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