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Priors for Bayesian adaptive spline smoothing

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  • Yu Yue
  • Paul Speckman
  • Dongchu Sun

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  • 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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    1. Stefan Lang & Eva-Maria Pronk & Ludwig Fahrmeir, 2002. "Function estimation with locally adaptive dynamic models," Computational Statistics, Springer, vol. 17(4), pages 479-499, December.
    2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    3. Alexandre Pintore & Paul Speckman & Chris C. Holmes, 2006. "Spatially adaptive smoothing splines," Biometrika, Biometrika Trust, vol. 93(1), pages 113-125, March.
    4. D. G. T. Denison & B. K. Mallick & A. F. M. Smith, 1998. "Automatic Bayesian curve fitting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 333-350.
    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, May.
    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. M. P. Wand, 2000. "A Comparison of Regression Spline Smoothing Procedures," Computational Statistics, Springer, vol. 15(4), pages 443-462, December.
    8. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    9. 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.
    10. Sally A. Wood, 2002. "Bayesian mixture of splines for spatially adaptive nonparametric regression," Biometrika, Biometrika Trust, vol. 89(3), pages 513-528, August.
    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, December.
    13. repec:dau:papers:123456789/1906 is not listed on IDEAS
    14. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
    15. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
    16. Paul L. Speckman, 2003. "Fully Bayesian spline smoothing and intrinsic autoregressive priors," Biometrika, Biometrika Trust, vol. 90(2), pages 289-302, June.
    17. Cummins D. J & Filloon T. G. & Nychka D., 2001. "Confidence Intervals for Nonparametric Curve Estimates: Toward More Uniform Pointwise Coverage," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 233-246, March.
    18. F. Abramovich & T. Sapatinas & B. W. Silverman, 1998. "Wavelet thresholding via a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(4), pages 725-749.
    19. 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.
    20. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
    21. 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.
    22. 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, May.
    23. 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, March.
    24. 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.
    25. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    2. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, Universität Innsbruck.
    3. James R. Faulkner & Andrew F. Magee & Beth Shapiro & Vladimir N. Minin, 2020. "Horseshoe‐based Bayesian nonparametric estimation of effective population size trajectories," Biometrics, The International Biometric Society, vol. 76(3), pages 677-690, September.
    4. 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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