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Information criterion for Gaussian change-point model

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  • Ninomiya, Yoshiyuki
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    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.

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    Bibliographic Info

    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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    Keywords: Akaike's information criterion Brownian motion with drift Maximum of random walk Number of change-points;

    References

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    1. 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.
    2. Yao, Yi-Ching, 1988. "Estimating the number of change-points via Schwarz' criterion," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 181-189, February.
    3. 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.
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    Cited by:
    1. Eiji Kurozumi & Purevdorj Tuvaandorj, 2010. "Model Selection Criteria in Multivariate Models with Multiple Structural Changes," Global COE Hi-Stat Discussion Paper Series gd10-144, Institute of Economic Research, Hitotsubashi University.
    2. Shen, Gang, 2013. "On empirical likelihood inference of a change-point," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1662-1668.
    3. Pan, Jianmin & Chen, Jiahua, 2006. "Application of modified information criterion to multiple change point problems," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2221-2241, November.

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