bartMachine: Machine learning with bayesian additive regression trees

Journal of Statistical Software

Volumn 70, Issue , 2016, Pages

  Kapelner, Adam ^a   Bleich, Justin ^b  

^a CITY UNIVERSITY OF NEW YORK   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Bayesian; Java; Machine learning; Non parametric; R; Statistical learning

Indexed keywords

EID: 84962381511     PISSN: 15487660     EISSN: None     Source Type: Journal    
DOI: 10.18637/jss.v070.i04     Document Type: Article

References (29)

1. Albert JH, Chib S (1993). "Bayesian Analysis of Binary and Polychotomous Response Data." Journal of the American Statistical Association, 88(422), 669-679. doi:10.1080/ 01621459.1993.10476321.
2. Bache K, Lichman M (2013). "UCI Machine Learning Repository." URL http://archive. ics.uci.edu/ml.
3. Blattenberger G, Fowles R (2014). "Avalanche Forecasting: Using Bayesian Additive Regression Trees (BART)." In Demand for Communications Services-Insights and Perspectives, pp. 211-227. Springer-Verlag.
4. Bleich J, Kapelner A, Jensen ST, George EI (2014). "Variable Selection for BART: An Application to Gene Regulation." The Annals of Applied Statistics, 8(3), 1750-1781. doi: 10.1214/14-aoas755.
5. Breiman L (2001). "Statistical Modeling: The Two Cultures." Statistical Science, 16(3), 199-231. doi:10.1214/ss/1009213726.
6. Chipman H, McCulloch R (2016). BayesTree: Bayesian Additive Regression Trees. R package version 0.3-1.3, URL https://CRAN.R-project.org/package=BayesTree.
7. Chipman H, McCulloch R, Dorie V (2014). dbarts: Discrete Bayesian Additive Regression Trees Sampler. R package version 0.8-5, URL https://CRAN.R-project.org/package= dbarts.
8. Chipman HA, George EI, McCulloch RE (2010). "BART: Bayesian Additive Regressive Trees." The Annals of Applied Statistics, 4(1), 266-298. doi:10.1214/09-aoas285.
9. Damien P, Dellaportas P, Polson N, Stephens D (2013). Bayesian Theory and Applications. 1st edition. Oxford University Press.
10. Ding Y, Simonoff JS (2010). "An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data." Journal of Machine Learning Research, 11, 131-170.
11. Eliashberg J (2010). Green-Lighting Movie Scripts: Revenue Forecasting and Risk Management. Ph.D. thesis, University of Pennsylvania.
12. Friedman J (2001). "Greedy Function Approximation: A Gradient Boosting Machine." The Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451.
13. Friedman JH (1991). "Multivariate Adaptive Regression Splines." The Annals of Statistics, 19, 1-67. doi:10.1214/aos/1176347963.
14. Friedman JH (2002). "Stochastic Gradient Boosting." Computational Statistics & Data Analysis, 38(4), 367-378. doi:10.1016/s0167-9473(01)00065-2.
15. Gelman A, Carlin JB, Stern HS, Rubin DB (2004). Bayesian Data Analysis. 2nd edition. Chapman & Hall / CRC.
16. Geman S, Geman D (1984). "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images." IEEE Transaction on Pattern Analysis and Machine Intelligence, 6, 721-741. doi:10.1109/tpami.1984.4767596.
17. Hastie T, Tibshirani R (2000). "Bayesian Backfitting." Statistical Science, 15(3), 196-213. doi:10.1214/ss/1009212815.
18. Hastings WK (1970). "Monte Carlo Sampling Methods Using Markov Chains and Their Applications." Biometrika, 57(1), 97-109. doi:10.2307/2334940.
19. Kapelner A, Bleich J (2015). "Prediction with Missing Data via Bayesian Additive Regression Trees." Canadian Journal of Statistics, 43(2), 224-239. doi:10.1002/cjs.11248.
20. Kapelner A, Bleich J (2016). bartMachine: Bayesian Additive Regression Trees. R package version 1.2.2, URL https://CRAN.R-project.org/package=bartMachine.
21. Kibler DF, Aha DW, Albert MK (1989). "Instance Based Prediction of Real Valued Attributes." Computational Intelligence, 5, 51. doi:10.1111/j.1467-8640.1989.tb00315.x.
22. Kindo B, Wang H, Pe E (2013). "MBACT-Multiclass Bayesian Additive Classification Trees." arXiv:1309.7821 [stat.ML], URL http://arxiv.org/abs/1309.7821v1.
23. Liaw A, Wiener M (2002). "Classification and Regression by randomForest." R News, 2(3), 18-22. URL https://CRAN.R-project.org/doc/Rnews/.
24. Pratola MT, Chipman H, Higdon D, McCulloch R, Rust W (2013). "Parallel Bayesian Additive Regression Trees." Technical report, University of Chicago.
25. Core Team (2016). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
26. Taddy MA, Gramacy RB, Polson NG (2011). "Dynamic Trees for Learning and Design." Journal of the American Statistical Association, 106(493), 109-123. doi:10.1198/jasa. 2011.ap09769.
27. Twala B, Jones M, Hand DJ (2008). "Good Methods for Coping with Missing Data in Decision Trees." Pattern Recognition Letters, 29(7), 950-956. doi:10.1016/j.patrec. 2008.01.010.
28. Urbanek S (2013). rJava: Low-Level R to Java Interface. R package version 0.9-6, URL https://CRAN.R-project.org/package=rJava.
29. Zhou Q, Liu J (2008). "Extracting Sequence Features to Predict Protein-DNA Interactions: A Comparative Study." Nucleic Acids Research, 36(12), 4137-4148. doi:10.1093/nar/ gkn361.