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Additive models in censored regression

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  • de Uña Álvarez, Jacobo
  • Roca Pardiñas, Javier
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    Abstract

    Additive models in censored regression are considered. A randomly weighted version of the backfitting algorithm that allows for the nonparametric estimation of the effects of the covariates on the response is provided. Given the high computational cost involved, binning techniques are used to speed up the computation in the estimation and testing process. Simulation results and the application to real data reveal that the predictor obtained with the additive model performs well, and that it is a convenient alternative to the linear predictor when some nonlinear effects are expected.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4VNH3M1-3/2/688a0c8704a829294405168816580dc7
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 53 (2009)
    Issue (Month): 9 (July)
    Pages: 3490-3501

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    Handle: RePEc:eee:csdana:v:53:y:2009:i:9:p:3490-3501

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    Web page: http://www.elsevier.com/locate/csda

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    1. Juan Carlos Pardo-Fernández & Ingrid Van Keilegom, 2006. "Comparison of Regression Curves with Censored Responses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 33(3), pages 409-434.
    2. Härdle, Wolfgang & Huet, Sylvie & Mammen, Enno & Sperlich, Stefan, 2001. "Bootstrap Inference in Semiparametric Generalized Additive Models," Finance Working Papers 01-3, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    3. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114.
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    5. Opsomer, Jean D., 2000. "Asymptotic Properties of Backfitting Estimators," Journal of Multivariate Analysis, Elsevier, vol. 73(2), pages 166-179, May.
    6. Opsomer, Jan & Ruppert, David, 1997. "Fitting a Bivariate Additive Model by Local Polynomial Regression," Staff General Research Papers 1071, Iowa State University, Department of Economics.
    7. Ali Gannoun & Jér�Me Saracco & Ao Yuan & George E. Bonney, 2005. "Non-parametric Quantile Regression with Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 32(4), pages 527-550.
    8. Sperlich, Stefan & Tjøstheim, Dag & Yang, Lijian, 1998. "Nonparametric estimation and testing of interaction in additive models," SFB 373 Discussion Papers 1998,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    9. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
    10. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    11. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
    12. Wensheng Guo, 2002. "Inference in smoothing spline analysis of variance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 887-898.
    13. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    14. Zheng, Shurong, 2008. "Selection of components and degrees of smoothing via lasso in high dimensional nonparametric additive models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 164-175, September.
    15. Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
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