Bayesian nonparametric quantile regression using splines

Computational Statistics and Data Analysis

Volumn 54, Issue 4, 2010, Pages 1138-1150

  Thompson, Paul ^a   Cai, Yuzhi ^a   Moyeed, Rana ^a   Reeve, Dominic ^a   Stander, Julian ^a  

^a UNIVERSITY OF PLYMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; CUBIC SPLINE; ENVIRONMENTAL DATA; MARKOV CHAIN MONTE CARLO ALGORITHMS; METROPOLIS-HASTINGS ALGORITHM; NON-PARAMETRIC; QUANTILE REGRESSION; SIMULATED DATA; SMOOTHING PARAMETER;

INFERENCE ENGINES; MARKOV PROCESSES; REGRESSION ANALYSIS; SPLINES;

BAYESIAN NETWORKS;

EID: 73149115061     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2009.09.004     Document Type: Article

References (33)

1. Bédard, M., 2006. Optimal acceptance rates for Metropolis-Hastings algorithms: Moving beyond 0.234. Annals of Statistics (submitted for publication)
2. Bédard M. Efficient sampling using algorithms. Journal of Computational and Graphical Statistics 17 2 (2008) 312-332
3. Bosch R., Ye Y., and Woodworth G.G. A convenient algorithm for quantile regression with smoothing splines. Computational Statistics and Data Analysis 19 (1995) 613-630
4. Brooks S.P., and Gelman A. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7 4 (1998) 434-455
5. Denison D.G.T., Mallick B.K., and Smith A.F.M. Automatic Bayesian curve fitting. Journal of the Royal Statistical Society: Series B 60 2 (1998) 333-350
6. Dias R., and Gamerman D. A Bayesian approach to hybrid splines non-parametric regression. Journal of Statistical Computing and Simulation 72 4 (2002) 285-297
7. Doksum K., and Koo J. On spline estimators and prediction intervals in nonparametric regression. Computational Statistics & Data Analysis 35 1 (2000) 67-82
8. Dunson D.B., and Taylor J.A. Approximate Bayesian inference for quantiles. Nonparametric Statistics 17 3 (2005) 385-400
9. Gamerman D. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (1997), Chapman and Hall, London
10. Gelman A. Inference and monitoring convergence. In: Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman and Hall, London 131-143
11. Gelman A., and Rubin D. Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7 (1992) 457-511
12. Green P.J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82 (1995) 711-732
13. Green P.J., and Silverman B.W. Nonparametric Regression and Generalized Linear Models (1994), Chapman and Hall, London
14. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer, New York
15. Kass R.E., Calin B.P., Gelman A., and Neal R.M. MCMC in practice: A roundtable discussion. The American Statistician: Statistical Practice 52 2 (1998) 93-100
16. Koenker R. Quantile Regression (2005), Cambridge University Press, Cambridge
17. Koenker R., Ng P., and Portnoy S. Quantile smoothing splines. Biometrika 81 4 (1994) 673-680
18. Koenker, R., 2008. quantreg: Quantile Regression (version 4.17). Vienna, Austria. http://www.r-project.org
19. Kottas A., and Gelfand A.E. Bayesian semiparametric median regression modeling. Journal of the American Statistical Association 96 456 (2001) 1458-1468
20. Kottas A., and Krnjajić M. Bayesian semiparametric modelling in quantile regression. Scandinavian Journal of Statistics 36 (2009) 297-319
21. Laurini F., and Pauli F. Smoothing sample extremes: The mixed model approach. Computational Statistics & Data Analysis 53 11 (2009) 3842-3854
22. Lejeune M.G., and Sarda P. Quantile regression: A nonparametric approach. Computational Statistics & Data Analysis 6 3 (1988) 229-239
23. Neal R.M. Slice sampling. Annals of Statistics 31 3 (2003) 705-767
24. Ng P. An algorithm for quantile smoothing splines. Computational Statistics & Data Analysis 22 2 (1996) 99-118
25. R Development Core Team,, 2009. R: A language and environment for statistical computing. Vienna, Austria. ISBN: 3-900051-07-0. http://www.R-project.org
26. Rao R.C. Linear Statistical Inference and its Applications. 2nd ed. (2002), John Wiley & Sons, New York
27. Reeve D., Chadwick A., and Fleming C. Coastal Engineering: Processes, Theory and Design Practice (2004), SPON, London
28. Silverman B.W. Some aspects of the spline smoothing approach to nonparametric curve fitting. Journal of the Royal Statistical Society: Series B 47 (1985) 1-52
29. Thompson, P., Reeve, D., Cai, Y., Stander, J., Moyeed, R., 2008. Bayesian nonparametric quantile regression using splines for modelling wave heights. In: FloodRisk 2008 Conference. Keble College, University of Oxford, UK
30. Venables W.N., and Ripley B.D. Modern Applied Statistics with S. 4th ed. (2002), Springer-Verlag, New York
31. Wang Y., Shao Q., and Zhu M. Quantile regression without the curse of unsmoothness. Computational Statistics & Data Analysis 53 10 (2009) 3696-3705
32. Yu K., and Moyeed R. Bayesian quantile regression. Statistics and Probability Letters 54 (2001) 437-447
33. Yu K., Lu Z., and Stander J. Quantile regression: Application and current research areas. Journal of the Royal Statistical Society: The Statistician 52 3 (2003) 331-350