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Some asymptotic results on generalized penalized spline smoothing

Author

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  • Göran Kauermann
  • Tatyana Krivobokova
  • Ludwig Fahrmeir

Abstract

Summary. The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non‐normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothing framework to generalized linear mixed models. We consider the asymptotic rates such that the Laplace approximation is justified and the resulting fits in the mixed model correspond to penalized spline estimates. Secondly, we make use of a fully Bayesian viewpoint by imposing an a priori distribution on all parameters and coefficients. We argue that with the postulated rates at which the spline basis dimension increases with the sample size the posterior distribution of the spline coefficients is approximately normal. The validity of this result is investigated in finite samples by comparing Markov chain Monte Carlo results with their asymptotic approximation in a simulation study.

Suggested Citation

  • Göran Kauermann & Tatyana Krivobokova & Ludwig Fahrmeir, 2009. "Some asymptotic results on generalized penalized spline smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 487-503, April.
    • Handle: RePEc:bla:jorssb:v:71:y:2009:i:2:p:487-503
    DOI: 10.1111/j.1467-9868.2008.00691.x
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    References listed on IDEAS

    as
    1. Yuedong Wang, 1998. "Mixed effects smoothing spline analysis of variance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 159-174.
    2. Kauermann, Goran, 2005. "Penalized spline smoothing in multivariable survival models with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 169-186, April.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, August.
    4. M. P. Wand, 2003. "Smoothing and mixed models," Computational Statistics, Springer, vol. 18(2), pages 223-249, July.
    5. Peter Hall & J. D. Opsomer, 2005. "Theory for penalised spline regression," Biometrika, Biometrika Trust, vol. 92(1), pages 105-118, March.
    6. X. Lin & D. Zhang, 1999. "Inference in generalized additive mixed modelsby using smoothing splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 381-400, April.
    7. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, August.
    8. 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.
    9. Hervé Cardot, 2002. "Local roughness penalties for regression splines," Computational Statistics, Springer, vol. 17(1), pages 89-102, March.
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