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Priors for Bayesian adaptive spline smoothing

Author

Listed:
  • Yu Yue

    ()

  • Paul Speckman

    ()

  • Dongchu Sun

    ()

Abstract

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Suggested Citation

  • Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 577-613, June.
  • Handle: RePEc:spr:aistmt:v:64:y:2012:i:3:p:577-613
    DOI: 10.1007/s10463-010-0321-6
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167.
    2. Alexandre Pintore & Paul Speckman & Chris C. Holmes, 2006. "Spatially adaptive smoothing splines," Biometrika, Biometrika Trust, vol. 93(1), pages 113-125, March.
    3. Paul L. Speckman, 2003. "Fully Bayesian spline smoothing and intrinsic autoregressive priors," Biometrika, Biometrika Trust, vol. 90(2), pages 289-302, June.
    4. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    5. Leonhard Knorr-Held & Sylvia Richardson, 2003. "A hierarchical model for space-time surveillance data on meningococcal disease incidence," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(2), pages 169-183.
    6. Carter, C.K. & Kohn, R., "undated". "Markov Chain Monte Carlo in Conditionally Gaussian State Space Models," Statistics Working Paper _003, Australian Graduate School of Management.
    7. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    8. Vrontos, I D & Dellaportas, P & Politis, D N, 2000. "Full Bayesian Inference for GARCH and EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 187-198, April.
    9. Sally A. Wood, 2002. "Bayesian mixture of splines for spatially adaptive nonparametric regression," Biometrika, Biometrika Trust, vol. 89(3), pages 513-528, August.
    10. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506.
    11. Ngo, Long & Wand, Matthew P., 2004. "Smoothing with Mixed Model Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i01).
    12. Finn Lindgren & Håvard Rue, 2008. "On the Second-Order Random Walk Model for Irregular Locations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 691-700.
    13. Nakatsuma, Teruo, 2000. "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach," Journal of Econometrics, Elsevier, vol. 95(1), pages 57-69, March.
    14. A. Brezger & L. Fahrmeir & A. Hennerfeind, 2007. "Adaptive Gaussian Markov random fields with applications in human brain mapping," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(3), pages 327-345.
    15. repec:dau:papers:123456789/1906 is not listed on IDEAS
    16. Sarat C. Dass & James O. Berger, 2003. "Unified Conditional Frequentist and Bayesian Testing of Composite Hypotheses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 193-210.
    17. Dongchu Sun & Paul Speckman, 2008. "Bayesian hierarchical linear mixed models for additive smoothing splines," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(3), pages 499-517, September.
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    Cited by:

    1. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, University of Innsbruck.
    2. Rakêt, Lars Lau & Markussen, Bo, 2014. "Approximate inference for spatial functional data on massively parallel processors," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 227-240.

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