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Selection of smoothing parameters inB-spline nonparametric regression models using information criteria

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  • Seiya Imoto
  • Sadanori Konishi

Abstract

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Suggested Citation

  • 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;The Institute of Statistical Mathematics, vol. 55(4), pages 671-687, December.
    • Handle: RePEc:spr:aistmt:v:55:y:2003:i:4:p:671-687
    DOI: 10.1007/BF02523388
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    Citations

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    Cited by:

    1. Anestis Antoniadis & Irène Gijbels & Mila Nikolova, 2011. "Penalized likelihood regression for generalized linear models with non-quadratic penalties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(3), pages 585-615, June.
    2. Kagerer, Kathrin, 2013. "A short introduction to splines in least squares regression analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 472, University of Regensburg, Department of Economics.
    3. Hidetoshi Matsui & Toshihiro Misumi, 2015. "Variable selection for varying-coefficient models with the sparse regularization," Computational Statistics, Springer, vol. 30(1), pages 43-55, March.
    4. Mitsunori Kayano & Koji Dozono & Sadanori Konishi, 2010. "Functional Cluster Analysis via Orthonormalized Gaussian Basis Expansions and Its Application," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 211-230, September.
    5. Masao Ueki & Kaoru Fueda, 2010. "Optimal tuning parameter estimation in maximum penalized likelihood method," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(3), pages 413-438, June.
    6. Shohei Tateishi & Sadanori Konishi, 2011. "Nonlinear regression modeling and detecting change points via the relevance vector machine," Computational Statistics, Springer, vol. 26(3), pages 477-490, September.
    7. Tomohiro Ando & Sadanori Konishi, 2009. "Nonlinear logistic discrimination via regularized radial basis functions for classifying high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 331-353, June.
    8. Ekheden, Erland & Hössjer, Ola, 2015. "Multivariate time series modeling, estimation and prediction of mortalities," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 156-171.

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