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Nonlinear regression modeling and detecting change points via the relevance vector machine

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  • Shohei Tateishi

    ()

  • Sadanori Konishi

    ()

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    Abstract

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    File URL: http://hdl.handle.net/10.1007/s00180-010-0220-6
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    Bibliographic Info

    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 26 (2011)
    Issue (Month): 3 (September)
    Pages: 477-490

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    Handle: RePEc:spr:compst:v:26:y:2011:i:3:p:477-490

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    Web page: http://www.springerlink.com/link.asp?id=120306

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    Related research

    Keywords: Basis expansion; Change point; Information criterion; Relevance vector machine; Nonlinear regression; Regularization;

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    1. Seiya Imoto & Sadanori Konishi, 2003. "Selection of smoothing parameters inB-spline nonparametric regression models using information criteria," Annals of the Institute of Statistical Mathematics, Springer, vol. 55(4), pages 671-687, December.
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    3. Irène Gijbels & Alexandre Lambert & Peihua Qiu, 2007. "Jump-Preserving Regression and Smoothing using Local Linear Fitting: A Compromise," Annals of the Institute of Statistical Mathematics, Springer, vol. 59(2), pages 235-272, June.
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