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Penalized likelihood regression for generalized linear models with non-quadratic penalties

Author

Listed:
  • Anestis Antoniadis
  • Irène Gijbels

    ()

  • Mila Nikolova

Abstract

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Suggested Citation

  • 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.
  • Handle: RePEc:spr:aistmt:v:63:y:2011:i:3:p:585-615
    DOI: 10.1007/s10463-009-0242-4
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Yu Y. & Ruppert D., 2002. "Penalized Spline Estimation for Partially Linear Single-Index Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1042-1054, December.
    3. Zhou S. & Shen X., 2001. "Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 247-259, March.
    4. Ming Yuan & Yi Lin, 2007. "On the non-negative garrotte estimator," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 143-161.
    5. 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.
    6. 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.
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    Citations

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    Cited by:

    1. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    2. Mandal, B.N. & Ma, Jun, 2016. "l1 regularized multiplicative iterative path algorithm for non-negative generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 289-299.
    3. Y. Andriyana & I. Gijbels & A. Verhasselt, 2014. "P-splines quantile regression estimation in varying coefficient models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 153-194, March.
    4. Irène Gannaz, 2013. "Wavelet penalized likelihood estimation in generalized functional models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 122-158, March.
    5. N. Neykov & P. Filzmoser & P. Neytchev, 2014. "Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator," Statistical Papers, Springer, vol. 55(1), pages 187-207, February.

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