Variable selection via a multi-stage strategy

Journal of Applied Statistics

Volumn 42, Issue 4, 2015, Pages 762-774

  Chang, Jing ^a   Lee, Herbert K H ^b  

^a University of North Carolina at Chapel Hill   (United States)

^b University of California Santa Cruz   (United States)

Author keywords

Gaussian process; regression tree; sensitivity analysis; sequential design; sum of tree

Indexed keywords

EID: 84921600811     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2014.985640     Document Type: Article

References (25)

1. H. Chipman and R. McCulloch, BayesTree: Bayesian Methods for Tree Based Models, R Package Version 0.3-1.1, 2010. Available at http://cran.r-project.org/web/packages/BayesTree/index.html.
2. H.A. Chipman, E.I. George, and R.E. McCulloch, Bayesian cart model search, J. Amer. Statist. Assoc. 93 (1998), pp. 935–948. doi: 10.1080/01621459.1998.10473750
3. H.A. Chipman, E.I. George, and R.E. McCulloch, BART: Bayesian additive regression trees, Ann. Appl. Stat. 4 (2010), pp. 266–298. doi: 10.1214/09-AOAS285
4. R.B. Gramacy, tgp: An R package for Bayesian nonstationary, semiparametric nonlinear regression and design by treed gaussian process models, J. Statist. Softw. 19 (2007), pp. 1–46.
5. R.B. Gramacy and H.K.H. Lee, Bayesian treed Gaussian process model with an application to computer modeling, J. Amer. Statist. Assoc. 103 (2008), pp. 1119–1130. doi: 10.1198/016214508000000689
6. R.B. Gramacy and H.K.H. Lee, Adaptive design and analysis of supercomputer experiments, Technometrics 51 (2009), pp. 130–145. doi: 10.1198/TECH.2009.0015
7. R.B. Gramacy and H.K.H. Lee, Cases for the nugget in modeling computer experiments, Stat. Comput. 22 (2012), pp. 713–722. doi: 10.1007/s11222-010-9224-x
8. R.B. Gramacy and M. Taddy, Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models, J. Statist. Softw. 33 (2010), pp. 1–48.
9. T. Hastie and R. Tibshirani, Bayesian backfitting, Statist. Sci. 15 (2000), pp. 196–223. doi: 10.1214/ss/1009212815
10. H. Ishwaran, J.S. Rao, and U.B. Kogalur, Spikeslab: Prediction and Variable Selection using Spike and Slab Regression, R Package Version 1.1.5, 2013. Available at http://cran.r-project.org/web/packages/spikeslab/index.html.
11. C. Linkletter, D. Bingham, N. Hengartner, D. Higdon, and K.Q. Ye, Variable selection for Gaussian process models in computer experiments, Technometrics 48 (2006), pp. 478–490. doi: 10.1198/004017006000000228
12. D.J.C. MacKay, Information-based objective functions for active data selection, Neural Comput. 4 (1992), pp. 589–611.
13. M. Morfin and D. Makowski, MMIX: Model Selection Uncertainty and Model Mixing, R Package Version 1.2, 2012. Available at http://cran.r-project.org/web/packages/MMIX/index.html.
14. M.D. Morris, Factorial sampling plans for preliminary computational experiments, Technometrics 33 (1991), pp. 161–174. doi: 10.1080/00401706.1991.10484804
15. J.E. Oakley, Decision-theoretic sensitivity analysis for complex computer models, Technometrics 51 (2009), pp. 121–129. doi: 10.1198/TECH.2009.0014
16. B.J. Reich, C.B. Storlie, and H.D. Bondell, Variable selection in Bayesian smoothing spline ANOVA models: Application to deterministic computer codes, Technometrics 51 (2009), pp. 110–120. doi: 10.1198/TECH.2009.0013
17. J. Sacks, W.J. Welch, T.J. Mitchell, and H.P. Wynn, Design and analysis of computer experiments, Statist. Sci. 4 (1989), pp. 409–423. doi: 10.1214/ss/1177012413
18. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola, Global Sensitivity Analysis: The Primer, John Wiley and Sons, Hoboken, NJ, 2008.
19. A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola, Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Comm. 181 (2010), pp. 259–270. doi: 10.1016/j.cpc.2009.09.018
20. T. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, Springer, New York, 2003.
21. S. Seo, M. Wallat, T. Graepel, and K. Obermayer, Gaussian process regression: Active data selection and test point rejection, Proceedings of the International Joint Conference on Neural Networks, Vol. III, IEEE, 2000, pp. 241–246.
22. M. Taddy, H.K.H. Lee, G.A. Gray, and J.D. Griffin, Bayesian guided pattern search for robust local optimization, Technometrics 51 (2009), pp. 389–401. doi: 10.1198/TECH.2009.08007
23. B. Tang, Latin Hypercube Designs, Wiley Online Library, Hoboken, NJ, 2008.
24. S. Thompson and G. Seber, Adaptive Sampling, John Wiley and Sons, Hoboken, NJ, 1996.
25. T. Zhang, Foba: Greedy Variable Selection, R Package Version 0.1, 2008. Available at http://cran.r-project.org/web/packages/foba/index.html.